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Medical Research Proposal Template: A Comprehensive Guide + Free Template Download + How to Write it

Medical research proposal template: a guide to crafting an effective proposal.

As a medical researcher, the process of developing a research proposal is essential for securing funding and approval for your study. A medical research proposal template provides a structured framework for outlining your study objectives , methods, and expected outcomes . It is crucial to understand the significance of a well-crafted proposal in conveying the potential impact of your research to stakeholders and funding organizations . In this article, we will explore the key elements of a medical research proposal template and how it differs from a standard proposal, providing you with the necessary tools to effectively communicate the importance of your research.

Medical Research Proposal Template

Example: the effect of exercise on blood pressure in hypertensive patients.

The objective of this study is to investigate the impact of regular exercise on blood pressure levels in patients diagnosed with hypertension. We will measure blood pressure before and after a 12-week exercise program to determine if there is a significant reduction in blood pressure.

My advice on the objective: Be specific and clearly state what you aim to achieve with your research. This will help reviewers understand the purpose of your study and its potential impact.

Recent studies have shown that regular physical activity can have a positive effect on blood pressure. However, the specific effects of exercise on hypertensive patients have not been thoroughly investigated. Understanding the relationship between exercise and blood pressure in this population could have significant implications for the management of hypertension.

My advice on the background: Provide a comprehensive review of existing literature to demonstrate the gap in knowledge that your study is addressing. This will show that your research is necessary and relevant.

Methodology

Participants: We will recruit 100 hypertensive patients from local clinics who are currently not engaged in a regular exercise program. Intervention: Participants will be randomly assigned to either a supervised exercise group or a control group. The exercise group will engage in a 30-minute moderate-intensity exercise program 5 days a week for 12 weeks, while the control group will maintain their regular activities. Outcome measures: We will assess blood pressure levels using a standardized protocol at baseline, 6 weeks, and 12 weeks.

My advice on the methodology: Clearly outline the study population, interventions, and outcome measures to ensure that the reviewers understand how you plan to conduct your research.

Data Analysis

We will use repeated measures analysis of variance (ANOVA) to compare the changes in blood pressure between the exercise and control groups over the 12-week period. We will also conduct subgroup analyses to explore potential differences based on age, gender, and baseline blood pressure levels.

My advice on data analysis : Choose appropriate statistical methods and explain why they are suitable for your study. This demonstrates that you have carefully considered how to analyze your data.

Implications

If our hypothesis is supported, the findings from this study could provide evidence for recommending regular exercise as a non-pharmacological intervention for managing hypertension. This could have significant implications for public health and clinical practice.

My advice on implications: Clearly state the potential impact of your findings and how they could benefit the field of medicine. This will help the reviewers understand the significance of your research.

• Personnel: $10,000
• Equipment: $5,000
• Participant recruitment : $3,000
• Data analysis : $7,000

My advice on the budget: Provide a detailed breakdown of the costs associated with your research to demonstrate that you have carefully considered the resources required for your study. This will help reviewers assess the feasibility of your proposal.

Download free Medical Research Proposal Template in Word DocX, Powerpoint PPTX, and PDF. We included Medical Research Proposal Template examples as well.

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Medical Research Proposal Template FAQ

1. what is the purpose of a medical research proposal.

The purpose of a medical research proposal is to outline the objectives, methodology, and significance of a proposed medical research study . It provides a detailed plan for conducting research and obtaining funding for the study.

2. What sections should be included in a medical research proposal?

A typical medical research proposal will include sections such as an introduction, literature review, research objectives, research methodology , anticipated outcomes, timeline, budget, and references.

3. How should the research objectives be presented in the proposal?

The research objectives should be clearly defined, specific, and achievable. They should address the research question and outline the goals of the study. Objectives should be presented in a bullet-point format to make them easily identifiable.

4. What should be included in the research methodology section?

The research methodology section should outline the proposed study design, data collection methods, data analysis techniques , and any ethical considerations . It should provide a detailed plan for how the research will be conducted and how the data will be analyzed.

5. How should the budget section be formatted?

The budget section should outline the anticipated costs of the research study, including personnel, equipment, supplies, travel, and any other expenses. It should be presented in a table format with itemized costs and justifications for each expense.

6. How should the timeline be presented in the proposal?

The timeline should be presented in a visual format, such as a Gantt chart , that outlines the key milestones and deadlines for the research study. It should clearly show the duration of each phase of the research project .

7. Are there specific formatting guidelines for a medical research proposal?

Most funding agencies and research institutions have specific formatting guidelines for research proposals. These guidelines typically include page limits, font size and style, margin requirements, and specific sections to be included. It’s important to carefully follow these guidelines when preparing a research proposal .

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What’s Included: Research Proposal Template

Our free dissertation/thesis proposal template covers the core essential ingredients for a strong research proposal. It includes clear explanations of what you need to address in each section, as well as straightforward examples and links to further resources.

The research proposal template covers the following core elements:

• Introduction & background (including the research problem)
• Literature review
• Research design / methodology
• Project plan , resource requirements and risk management

The cleanly-formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

PS – if you’d like a high-level template for the entire thesis, you can we’ve got that too .

Research Proposal Template FAQS

What types of research proposals can this template be used for.

The proposal template follows the standard format for academic research projects, which means it will be suitable for the vast majority of dissertations and theses (especially those within the sciences), whether they are qualitative or quantitative in terms of design.

Keep in mind that the exact requirements for the introduction chapter/section will vary between universities and degree programs. These are typically minor, but it’s always a good idea to double-check your university’s requirements before you finalise your structure.

Is this template for an undergrad, Master or PhD-level proposal?

This template can be used for a research project at any level of study. Doctoral-level projects typically require the research proposal to be more extensive/comprehensive, but the structure will typically remain the same.

How long should my research proposal be?

The length of a research proposal varies by institution and subject, but as a ballpark, it’s usually between 1,500 and 3,000 words.

To be safe, it’s best to check with your university if they have any preferences or requirements in terms of minimum and maximum word count for the research propsal.

How detailed should the methodology of the proposal be?

You don’t need to go into the fine details of your methodology, but this section should be detailed enough to demonstrate that your research approach is feasible and will address your research questions effectively. Be sure to include your intended methods for data collection and analysis.

Can I include preliminary data or pilot study results in my proposal?

Generally, yes. This can strengthen your proposal by demonstrating the feasibility of your research. However, make sure that your pilot study is approved by your university before collecting any data.

Can I share this template with my friends/colleagues?

Yes, you’re welcome to share this template in its original format (no editing allowed). If you want to post about it on your blog or social media, we kindly request that you reference this page as your source.

What format is the template (DOC, PDF, PPT, etc.)?

The research proposal template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

Do you have templates for the other chapters?

Yes, we do. We are constantly developing our collection of free resources to help students complete their dissertations and theses. You can view all of our template resources here .

Can Grad Coach help me with my dissertation/thesis?

Yes, you’re welcome to get in touch with us to discuss our private coaching services .

Further Resources: Proposal Writing

The template provides step-by-step guidance for each section of your research proposal, but if you’d like to learn more about how to write up a high-quality research proposal, check out the rest of our free proposal-related resources:

• Research Proposal 101
• Examples of research proposals
• How To Find A Research Topic
• How To Find A Research Gap
• Developing Your Golden Thread
• How To Write A Research Proposal
• 8 Common Proposal Writing Mistakes

You can also visit the Grad Coach blog for more proposal-related resources.

If you’d prefer 1-on-1 support with your research proposal, have a look at our private coaching service , where we hold your hand through the research process, step by step.

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Home » How To Write A Research Proposal – Step-by-Step [Template]

How To Write A Research Proposal – Step-by-Step [Template]

Table of Contents

How To Write a Research Proposal

Writing a Research proposal involves several steps to ensure a well-structured and comprehensive document. Here is an explanation of each step:

1. Title and Abstract

• Choose a concise and descriptive title that reflects the essence of your research.
• Write an abstract summarizing your research question, objectives, methodology, and expected outcomes. It should provide a brief overview of your proposal.

2. Introduction:

• Provide an introduction to your research topic, highlighting its significance and relevance.
• Clearly state the research problem or question you aim to address.
• Discuss the background and context of the study, including previous research in the field.

3. Research Objectives

• Outline the specific objectives or aims of your research. These objectives should be clear, achievable, and aligned with the research problem.

4. Literature Review:

• Conduct a comprehensive review of relevant literature and studies related to your research topic.
• Summarize key findings, identify gaps, and highlight how your research will contribute to the existing knowledge.

5. Methodology:

• Describe the research design and methodology you plan to employ to address your research objectives.
• Explain the data collection methods, instruments, and analysis techniques you will use.
• Justify why the chosen methods are appropriate and suitable for your research.

6. Timeline:

• Create a timeline or schedule that outlines the major milestones and activities of your research project.
• Break down the research process into smaller tasks and estimate the time required for each task.

7. Resources:

• Identify the resources needed for your research, such as access to specific databases, equipment, or funding.
• Explain how you will acquire or utilize these resources to carry out your research effectively.

8. Ethical Considerations:

• Discuss any ethical issues that may arise during your research and explain how you plan to address them.
• If your research involves human subjects, explain how you will ensure their informed consent and privacy.

9. Expected Outcomes and Significance:

• Clearly state the expected outcomes or results of your research.
• Highlight the potential impact and significance of your research in advancing knowledge or addressing practical issues.

10. References:

• Provide a list of all the references cited in your proposal, following a consistent citation style (e.g., APA, MLA).

11. Appendices:

• Include any additional supporting materials, such as survey questionnaires, interview guides, or data analysis plans.

Research Proposal Format

The format of a research proposal may vary depending on the specific requirements of the institution or funding agency. However, the following is a commonly used format for a research proposal:

1. Title Page:

• Include the title of your research proposal, your name, your affiliation or institution, and the date.

2. Abstract:

• Provide a brief summary of your research proposal, highlighting the research problem, objectives, methodology, and expected outcomes.

3. Introduction:

• Introduce the research topic and provide background information.
• State the research problem or question you aim to address.
• Explain the significance and relevance of the research.
• Review relevant literature and studies related to your research topic.
• Summarize key findings and identify gaps in the existing knowledge.
• Explain how your research will contribute to filling those gaps.

5. Research Objectives:

• Clearly state the specific objectives or aims of your research.
• Ensure that the objectives are clear, focused, and aligned with the research problem.

6. Methodology:

• Describe the research design and methodology you plan to use.
• Explain the data collection methods, instruments, and analysis techniques.
• Justify why the chosen methods are appropriate for your research.

7. Timeline:

8. Resources:

• Explain how you will acquire or utilize these resources effectively.

9. Ethical Considerations:

• If applicable, explain how you will ensure informed consent and protect the privacy of research participants.

10. Expected Outcomes and Significance:

11. References:

12. Appendices:

Research Proposal Template

Here’s a template for a research proposal:

1. Introduction:

2. Literature Review:

3. Research Objectives:

4. Methodology:

5. Timeline:

6. Resources:

7. Ethical Considerations:

8. Expected Outcomes and Significance:

9. References:

10. Appendices:

Research Proposal Sample

Title: The Impact of Online Education on Student Learning Outcomes: A Comparative Study

1. Introduction

Online education has gained significant prominence in recent years, especially due to the COVID-19 pandemic. This research proposal aims to investigate the impact of online education on student learning outcomes by comparing them with traditional face-to-face instruction. The study will explore various aspects of online education, such as instructional methods, student engagement, and academic performance, to provide insights into the effectiveness of online learning.

2. Objectives

The main objectives of this research are as follows:

• To compare student learning outcomes between online and traditional face-to-face education.
• To examine the factors influencing student engagement in online learning environments.
• To assess the effectiveness of different instructional methods employed in online education.
• To identify challenges and opportunities associated with online education and suggest recommendations for improvement.

3. Methodology

3.1 Study Design

This research will utilize a mixed-methods approach to gather both quantitative and qualitative data. The study will include the following components:

3.2 Participants

The research will involve undergraduate students from two universities, one offering online education and the other providing face-to-face instruction. A total of 500 students (250 from each university) will be selected randomly to participate in the study.

3.3 Data Collection

The research will employ the following data collection methods:

• Quantitative: Pre- and post-assessments will be conducted to measure students’ learning outcomes. Data on student demographics and academic performance will also be collected from university records.
• Qualitative: Focus group discussions and individual interviews will be conducted with students to gather their perceptions and experiences regarding online education.

3.4 Data Analysis

Quantitative data will be analyzed using statistical software, employing descriptive statistics, t-tests, and regression analysis. Qualitative data will be transcribed, coded, and analyzed thematically to identify recurring patterns and themes.

4. Ethical Considerations

The study will adhere to ethical guidelines, ensuring the privacy and confidentiality of participants. Informed consent will be obtained, and participants will have the right to withdraw from the study at any time.

5. Significance and Expected Outcomes

This research will contribute to the existing literature by providing empirical evidence on the impact of online education on student learning outcomes. The findings will help educational institutions and policymakers make informed decisions about incorporating online learning methods and improving the quality of online education. Moreover, the study will identify potential challenges and opportunities related to online education and offer recommendations for enhancing student engagement and overall learning outcomes.

6. Timeline

The proposed research will be conducted over a period of 12 months, including data collection, analysis, and report writing.

The estimated budget for this research includes expenses related to data collection, software licenses, participant compensation, and research assistance. A detailed budget breakdown will be provided in the final research plan.

8. Conclusion

This research proposal aims to investigate the impact of online education on student learning outcomes through a comparative study with traditional face-to-face instruction. By exploring various dimensions of online education, this research will provide valuable insights into the effectiveness and challenges associated with online learning. The findings will contribute to the ongoing discourse on educational practices and help shape future strategies for maximizing student learning outcomes in online education settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Preparation of the Investigator for a Proposal

The research proposal, insights into the reviewer's perspective, conclusions, writing successful research proposals for medical science  .

(Schwinn) Professor of Anesthesiology and Surgery; Associate Professor of Pharmacology/Cancer Biology, Duke University Medical Center; Senior Fellow, Duke Pepper Aging Center.

(DeLong) Associate Professor, Division of Biometry and Medical Informatics, Duke University Medical Center.

(Shafer) Staff Anesthesiologist, Palo Alto VA Health Care System; Associate Professor of Anesthesia, Stanford University.

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Debra A. Schwinn , Elizabeth R. DeLong , Steven L. Shafer; Writing Successful Research Proposals for Medical Science   . Anesthesiology 1998; 88:1660–1666 doi: https://doi.org/10.1097/00000542-199806000-00031

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HIGH-QUALITY research proposals are required to obtain funds for the basic and clinical sciences. In this era of diminishing revenues, the ability to compete successfully for peer-reviewed research money is essential to create and maintain scientific programs. Ideally, the essentials of “grantsmanship” are learned through observation and participation in grant preparation, but the training environment experienced by most physicians typically focuses on clinical skills. Most physicians are never exposed to a research environment and therefore do not learn how to write grants. The result is that many clinical studies, even when designed by skilled clinicians and those that address important clinical questions, often do not compete successfully with proposals written by basic scientists. This creates a perception that clinical studies are not favorably viewed by research review committees. The opposite is probably closer to the truth; research review committees are very keen to fund excellent clinical research. Although greater numbers of researchers with Ph.D. degrees have applied for National Institutes of Health (NIH) grants compared with researchers with M.D. degrees over the last 10 yr, funding rates (percent applications funded) have remained approximately the same for these investigators ( Figure 1 ; 1995 success rates: all degrees, 6,759 [26.8%]; M.D. - Ph.D., 370 [23.1%]; M.D., 1,518 [28.1%]; Ph.D., 4,746 [26.8%]; other degree, 125 [23.1%]).[section]

Figure 1. Overall success rates for NIH funding of scientific applications, 1986 - 1995. No difference in funding rate is observed between applicants holding M.D. versus Ph.D. degrees. As the success rate for first-time applications was 11.3% in 1993, it is apparent that resubmission of a revised application significantly increases the overall chance of having research proposal ultimately funded.[section]

Capable medical researchers ultimately write research proposals for funding by the NIH. Standards of excellence for NIH grants are high (only the top [almost equal to] 20% of grants are funded). Research questions posed must be hypothesis driven; the investigator must be qualified to perform the study; and preliminary evidence should be presented demonstrating that the research is feasible and will answer the questions posed. The goal of this article is to review important elements of successful research proposals, with emphasis on funding sources available to the anesthesiology community. Two important anesthesia-specific organizations exist to support anesthesia research - The Foundation for Anesthesia Education and Research (FAER, an organization under the auspices of the American Society of Anesthesiologists) and the International Anesthesiology Research Society (IARS).

Successful applications for research support from FAER and IARS have many of the characteristics of grants funded by the NIH and other peer-reviewed funding sources. These characteristics include (1) a highly qualified investigator(s);(2) for junior investigators, a mentor with a successful track record in scientific investigation, peer-reviewed funding, and mentorship of fellows and faculty;(3) a supportive academic environment; and (4) a scientifically sound proposal. Each of these characteristics is discussed in the subsequent sections.

Training of the Investigator

One of the most important components of a successful research proposal is a well-trained investigator. Training in clinical anesthesia is not training in research methodology or scientific thinking; it does not prepare an individual for a career in investigation. Although obvious for basic science research, clinical research also requires commitment of a minimum of 1 yr of dedicated training with a good mentor, and more typically 2 - 3 yr in the field of the proposed research. The applicant also needs to demonstrate commitment to a career in investigation. Several years of scientific training is the first demonstration of such commitment. Research proposals must document institutional support for nonclinical time, and the investigator must provide evidence that this time has been used wisely and will continue to be dedicated to the proposed research.

The research proposal must document a track record of productivity by the investigator. This expectation increases as the training and career of the investigator progresses. Fellowship awards do not have an expectation of prior research training, so publications from prior research are not expected. At the fellowship level, outstanding letters of recommendation, undergraduate and medical school performance, and related accomplishments are most important. Because previous training is not required of the fellowship applicant, prior success of the mentor (publications and track record with previous trainees) weighs heavily in the fellowship review. For junior faculty, peer-reviewed publications are expected from the fellowship period. Young Investigator Annoucements (from FAER) and several new IARS awards require several years as a successful junior faculty member, so expectations of demonstrated research success are further increased. The investigator must demonstrate (1) rigorous training, (2) commitment to research, (3) an appropriate career path, and (4) a track record of productive work. None of these are trivial issues, and none can be easily accomplished without making a commitment to research early in the academic career.

The quality of the mentor is another important aspect of awards granted to fellows and junior faculty. Identification of a mentor is explicitly required for FAER and certain junior level NIH grant applications. First and foremost, the mentor must be a successful investigator. Criteria for this include a track record of publication in the area of the proposed research, continued peer-reviewed funding, and a history of successfully training young investigators. Although mentorship is not considered heavily in more senior grant applications, input from a more experienced investigator often remains beneficial throughout one's career (as we can personally attest to). In addition to the mentor, high-quality coinvestigators, collaborators, and consultants also play important roles in strengthening a research proposal.

Environment

Good research is best accomplished in a supportive, cooperative environment. Because of the changing climate of clinical medicine, researchers (both clinical and basic science) face increasing pressure to minimize research time. It is not possible to become a successful investigator in one's spare time. Documentation of adequate nonclinical time for research (not for committee meetings or other unrelated tasks) is essential. Receiving funding at a junior level often enables the department to match funds or to guarantee nonclinical time to the budding investigator. In general, the more non-clinical time available to an investigator, the more competitive the application.

Other important elements of the environment include people, space, and institutional resources. People include mentors, consultants who can help with specific methodologies, statistical support, helpful colleagues, experienced technicians, a clinical research team, and a dedicated chairperson. There must be adequate space for performing the proposed studies, office space for research personnel, and storage space for equipment and supplies. Institutional resources include related departmental and interdepartmental seminar series, a critical mass of investigators in a related area, instrument development and repair shops, and necessary laboratory space and common facilities.

Criteria for a sound research proposal are the same whether the proposal is submitted to NIH, FAER, IARS, or other funding sources. In crafting a proposal, it is essential to consider the perspective of the reviewer; therefore, items of interest to the reviewer are listed after general definition of the grant proposal.

Review committees receive dozens of grants. NIH study sections may review as many as 150 proposals during one session. Typically, only two or three reviewers are assigned to read each grant in detail, but everyone is expected to read each abstract. Hence, the abstract is often one of the most important parts of the research proposal. The abstract should address the significance of the question and the overall topic, state the hypothesis, and point out key preliminary data. Additionally, the abstract should provide a synopsis of methodologies planned. In the end, the reviewer must be convinced that the applicant is uniquely (or ideally) suited to undertake this important study by the end of this concise paragraph.

Body of the Grant

Specific Aims. The specific aims section is critically important in a scientific proposal. It is here that the investigator crystallizes the overall goal of the research and states specific hypotheses.

Beginning with the specific aims, the proposal must be well written and logically organized. A poorly organized grant application is difficult to review, even if the science is otherwise excellent. Typically, the specific aims begin with a short introduction (one paragraph), followed by a formally stated hypothesis. The hypothesis must be answerable by the research methods proposed. Generally, two or three specific aims are outlined with subheadings where appropriate. Organization of the specific aims is often temporal, starting with a proposed mechanism or the first set of studies in a clinical project. In general, the specific aims section should be no longer than one page.

Background and Significance. The background section provides an opportunity to bring reviewers up to date on current research in the area of the proposal. This section should summarize succinctly studies from the literature and related work published by the investigator. The most crucial aspect of the background is to build a case for significance of the proposed research regarding the ultimate clinical application or mechanistic understanding. Ideally, the background section should demonstrate that the current proposal is a logical extension of previous studies in the field and will provide new information and novel insights. In general, the background section should be about one fourth of the length of the grant proposal.

Preliminary Data. Preliminary data provide the opportunity for the investigator to demonstrate his or her ability to perform the proposed research. The goal in presenting preliminary data is to convince the reviewer that the investigator is capable of performing the proposed studies and that the mechanisms proposed are plausible. Good preliminary data support novel (or even unlikely) hypotheses. Each experimental method proposed should be accompanied by preliminary data demonstrating facility and expertise with related preparations. For example, if the investigator proposes using a specific electrophysiologic technique to study an ion channel, evidence demonstrating that this technique has been used by the investigator with other ion channels and a Figure showingresults from pilot experiments on the channel of interest would suffice. In clinical studies, demonstration of a working investigative team and the ability to enroll a given number of patients per week is helpful. Figures or tables help to convey the message in a succinct manner. They also conserve space in the proposal and create a more impressive effect. Although it is best if the applicant has generated his or her own preliminary data, for training awards, preliminary data from the mentor's laboratory is entirely appropriate. An effective way to organize preliminary data is to present it in the same order as the specific aims (e.g., C.1 preliminary data corresponds to A.1 specific aims, C.2 preliminary data corresponds to A.2 specific aims, etc.). Presentation of preliminary data usually takes about one fourth to one third of the length of the grant application.

Methods. The methods are the guts of the research proposal. Unfortunately, many investigators run out of steam by the time they reach the methods, leaving reviewers unconvinced by the proposed methodology. Ideally, the model being investigated should be broken down into simple, logical components, each accompanied by a description of specific experiments/interventions to be performed. The investigator should assume that at least one reviewer is an expert in each method presented. Therefore, enough detail should be provided to convince an expert that the experiment or technique is being performed properly. Methods presented as a list of recipes, requiring the reviewer to guess which method applies to each study, are recipes for disaster. Individual experimental techniques should be state of the art. In addition, approaching a problem from several angles is often helpful. “Lingo” of the field should be avoided; it is very annoying to reviewers to have to look up unexplained abbreviations or to have models alluded to rather than described. For training grants, methods should involve techniques currently being performed in the laboratory of the mentor. An effective way to organize the methods section is to follow the same order as the preliminary data and specific aims sections (e.g., D.1 methods corresponds to C.1 preliminary data and A.1 specific aims, etc.).

The methods sections should include a description of the design, conduct, and analysis of each study being proposed. Common errors in design include lack of specification of primary outcome, lack of randomization or blinding in clinical trials, inadequate justification of sample size, failure to adjust the total study number for expected dropouts/failed experiments or patient refusal, and use of single drug doses or concentrations rather than development of dose - response or concentration - response relations. Common errors in conducting research include lack of confirmation of drug concentrations, inadequate reproducibility of final results, lack of standardization of procedures, inadequate follow-up, incomplete data recording, and overall lack of organization.

Inadequate or inappropriate statistical methods can be a major weakness of a grant proposal. Many investigators feel confident with all aspects of their methods except the statistical section. Because statistical issues underlie the design and analysis strategy for every study, the input of a biostatistician is essential in planning the research and writing the grant application. Statistical considerations include specification of the primary end points that drive power calculations. Common statistical errors in research proposals include lack of sample size/power calculations, treating continuous variables as dichotomous, repeated t tests when a more comprehensive modeling approach should be taken, application of statistical tests that assume normality without verifying assumptions, failure to consider covariate effects, and failure to distinguish between interindividual and intraindividual variability. The investigator should be familiar with the concept of statistical power and be prepared to estimate some of the quantities needed to formulate an alternative hypothesis appropriately. The statistical analysis should be clearly outlined with specific methodology directed toward the hypotheses of the study. A statistical reviewer is unlikely to be convinced by a statement that “appropriate statistical methodology will be used” or by a barrage of nonspecific statistical jargon. At least one full paragraph (and sometimes an entire page) of the research proposal should be devoted to statistical analysis. Often several smaller statistics sections are appropriately included after each method is presented.

Even the best methods have potential problems and weaknesses. It is critical that the methods section discuss potential problems that may be encountered during the study and state how the investigator proposes to deal with these problems creatively. Reviewers tend to be impressed when the investigator presents potential problems that never occurred to them, because it suggests that the investigator is an expert in this area of research. A time line and organizational plan (who will be responsible for what) should also be included in the methods section so the reviewers can determine whether the investigator is being realistic in his or her approach. The methods section is typically one third to one half of the length of the entire grant proposal.

Introduction to Revised Application. Because so few grant applications are funded on their first submission (11.5% in 1993), the new investigator should not be unduly alarmed if his or her application is not funded. When a grant application has been unsuccessful, an investigator should revise the application and reapply, even if the original score was “noncompetitive”(meaning the grant was in the lower 50% of applications). Often the reviewers suggest key changes that will improve the application significantly. When submitting a revised application, an introduction (placed before the specific aims section) is used to discuss how criticisms of the original grant have been addressed in the revised proposal. Because the reviewer's comments are intended to be helpful, it is important to address each concern carefully in the revised proposal (changed text should be highlighted in the revised application by italic, bold, or identifying lines in the margin), with changes outlined in the introduction section. Angry responses to reviewers do not facilitate funding of the revised application. Remember that reviewers usually have a copy of the prior review, and they expect corrections or, when appropriate, an explanation of why you have chosen not to incorporate some suggestions from a prior review. Time taken to revise an application is well spent; as Figure 1 demonstrates, investigators who persist in revising and resubmitting their applications have an increased chance ([almost equal to] 20% with no previous NIH support, [almost equal to] 35% if previously funded) of ultimately being funded.[section]

In writing a research grant, it is helpful to consider the reviewer's perspective. Key features considered by reviewers include significance, approach, and feasibility. It is wise for the investigator to reread his or her application before submission with these features in mind. The NIH recently has published two documents on-line that discuss review criteria; examination of these documents before submission of a research proposal may prove helpful. These include the Report of the Committee on Rating Grant Applications[double vertical bar] and Review Criteria for Rating Unsolicited Research Grants.#

Significance

First and foremost, is the investigator asking an important question? There are two general ways research studies can be significant. The first is to demonstrate clinical significance. The litmus test for clinical significance is whether the proposed research will improve patient care. The second is elucidation of fundamental mechanisms underlying disease or biologic processes. The ideal research question succeeds in being significant in both areas.

The reviewer assesses whether the research plan can support or refute the stated hypothesis. In addition, the reviewer assesses whether the methodologies used provide adequate or, better yet, elegant approaches to the problem. Recently, the NIH has mandated an increasing emphasis on innovation in research. [1] **

Review committees generally are composed of individuals with expertise in many scientific areas. Additionally, study sections often retain outside reviewers with expertise in the proposed research area. The investigator should assume that his or her methods will be critiqued by at least one expert. Therefore, the investigator should not propose a method that would strike the world's expert in the field as being simplistic, inappropriate, or nonsensical, because the world's expert just might be one of the reviewers. Conversely, some reviewers do not have expertise in the proposed area of research. To ensure that the nonexpert is convinced of the validity and importance of proposed methodologies, the overall proposal should be written with a logical flow of ideas that build from basic to sophisticated concepts. Beginning each portion of the methods section with a short introduction for the nonexpert, followed by a more detailed description of the proposed methods, is an effective strategy to address the needs of both expert and nonexpert reviewers.

Feasibility

The investigator must convince reviewers that the chosen approach is feasible. Preliminary data provide the best demonstration of feasibility. Feasibility is often demonstrated by a track record of publications or peer-reviewed grant support for the applicant or mentor using the proposed experimental approach. Feasibility also can be demonstrated by appropriate statistical analysis of the proposal. For example, a power analysis and corresponding data on the number of patients with the required characteristics at the investigator's institution helps convince reviewers that a clinical study is feasible.

Anesthesiology Funding Sources

Funding for research performed by anesthesiologists is available from many sources. Because the discipline of anesthesiology overlaps many other fields, anesthesiologists have the opportunity to apply for research funds from agencies as diverse as the American Academy of Pediatrics, American Cancer Society, American Heart Association (national and local), American Thoracic Society, American Society for Regional Anesthesiology, critical care societies, Department of Veterans Affairs, National Science Foundation, Shriners, Society for Cardiovascular Anesthesiology, Society for Obstetrics and Perinatology, National Aeronautics and Space Aviation, NIH, and many other private foundations. Grants from FAER and IARS are available specifically to the anesthesiology community.

It is important that anesthesiologists continue to apply for NIH grants. For fiscal year 1996, the NIH awarded 149 research grants (including career development grants, R29, R01, and program project grants) to departments of anesthesiology, totaling $21 million in direct costs ([almost equal to]$31 million in total costs). Because of the diversity of research projects in anesthesiology, these grants were awarded by 14 different institutes, centers, and divisions within the NIH. In analyzing data for three recent review sessions (June 1996, October 1996, and February 1997) from the surgery, anesthesiology, and trauma study section, 26% of anesthesiology applications scored in the top 20th percentile, and 31% scored in the top 25th percentile; clearly no bias exists against anesthesiology in this predominantly surgical study section, at least in this limited sample (Alison Cole, anesthesiology representative for the National Institute of General Medicine Science at the NIH, personal communication, December, 1997). Table 1  

Table 1. Number of Recipients of NIH Research Project Annoucements  

A brief list of funding opportunities available to anesthesiologists early in their career is shown in Table 2 . Several sites are available on the World Wide Web ( Table 3 ) to facilitate access to grant/training resources for anesthesiologists. We have created an additional website ( http://pkpd.icon.palo-alto.med.va.gov/grants/grants.htm ), which provides access to more comprehensive lists of funding agencies and direct links to funding sources. This website also contains example grants designed to illustrate the grant writing principles discussed in this article.

Table 2. Potential Funding Sources  

Table 3. Grant/Training Resources on the WWW  

Successful grant applications require a well-trained investigator who carefully outlines a hypothesis-driven research proposal. Unique to FAER and IARS research committees is that the reviewers are mostly investigators and practicing anesthesiologists. These reviewers fully appreciate the importance of clinical research and enthusiastically support high-quality clinical studies. Although descriptive clinical studies are interesting to practicing clinicians, from a scientific perspective, clinical research must be driven by testable hypotheses. Without a testable hypothesis, clinical research cannot pass the test of adequate significance required for funding.

It is our hope that by demystifying the grant writing and review process that more anesthesiologists will be encouraged to submit proposals for research funding. As part of this effort, we strongly encourage residents and fellows interested in research careers to obtain adequate research training and to apply for appropriate fellowship/junior faculty awards early in their careers.

[section] NIH Extramural Data and Trends, Fiscal Years 1986 - 1995. Bethesda, Office of Reports and Analysis (component of the Office of Extramural Research), National Institutes of Health. (Published on-line and periodically updated. http://www.nih.gov/grants/award/award.htm ).

[double vertical bar] Report of the Committee on Rating Grant Applications. Revised 5/17/96. Bethesda, National Institutes of Health. (Published on-line. http://www.nih.gov/grants/peer/rga.pdf ).

# Review Criteria for Rating Unsolicited Research Grants. NIH Guide, Vol. 26, No. 22, 6/27/97. Bethesda, National Institutes of Health. (Published on-line. http://www.nih.gov/grants/guide/1997/97.06.27/notice-review-criter9.html ).

** Brown KS: A winning strategy for grant application: Focus on impact. The Scientist 1997; April 8:13–4

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Medical Research Proposal Sample & Guide

Table of Contents

A medical research proposal sample is a great way to understand what your proposal should look like. It can give you the structure and guidance needed to create a successful proposal.  Well-written medical research proposals help researchers stand out from other applicants and boost their chances of being selected or funded. Read on to find out what a medical research proposal entails and how to write yours with our easy sample.

What Is a Medical Research Proposal?

A medical research proposal is a document that outlines the purpose and methodology of a proposed research project . It includes information about what the researcher intends to study, how they plan to conduct their research and measurement for success or failure.  The proposal also explains why the research is essential, what ethical considerations need to be considered, and the potential risks associated with it. 

Why Is a Medical Research Proposal Necessary?

A medical research proposal is essential because it outlines the critical information and details necessary to complete a project successfully.  This document ensures that everyone understands their position and how they will contribute resources, time, and effort to make the project successful. It also helps researchers to secure funding from sponsors and provide transparency for potential participants in the study. 

What to Include in a Medical Research Proposal?

A medical research proposal should include the following:

• A brief description of the project, outlining the purpose and goals. 
• An explanation of how you plan to collect data; through surveys or interviews with participants?
• List any ethical considerations involved, including who will have access to the collected information and how it will be stored securely. 
• Budget required for the research and any timeline associated with the completion of the study. 
• Samples from past researchers, so you can learn more about what makes a successful medical research proposal. 

Steps on How to Write a Medical Research Proposal

It is important to remember that all proposals, no matter the topic, should follow specific steps to make them effective and organized. Here are a few steps to guide you:

Brainstorm and Outline the Problem

The first step is to brainstorm the project you are proposing, making sure that it has relevance in medicine. Determine what issue you are trying to address through your research and explain it clearly as a problem statement. 

Describe Your Project

Provide detailed information on your project, the aims, the expected outcomes, and any methods used to achieve them. 

Break It Into Small Sections

Once you have a clear vision of what you want to accomplish, break it down into small sections to effectively convey your thoughts. 

Set Your Objectives

Set specific objectives for your project and explain how you plan to achieve them. 

Outline Your Methodology

 Describe the processes used to collect data, analyze results, and draw conclusions from the research. 

Discuss Ethical Considerations

Explain any ethical considerations relevant to your proposed project, such as privacy and consent.

Write a Budget

Outline the cost of the project, including any equipment or materials needed.

Proofread the Proposal

Make sure to read through your proposal carefully before submitting it and ask someone else to do so, if possible. 

Medical Research Proposal Sample

To help you get started with your medical research proposal, here is an example: Project Aims:  This project aims to study air pollution’s effects on public health in a particular city.  Objectives:  To investigate how air pollution impacts public health in the target city and how to mitigate it. Methodology:  Data will be collected through surveys, interviews with residents, and environmental air quality sampling.  Ethical Considerations:  All participants in the project must be informed of the risks involved and consent to its use for research purposes. Personal information will be kept confidential and only used for research purposes.  Budget:  The budget allocated for this project is $5000. 

Developing a medical research proposal requires careful consideration and organization. Consequently, a medical research proposal sample might serve as an excellent starting point when writing your own project .

Abir Ghenaiet

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

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To create a medical research proposal, you need to find valuable information and create a complicated document. The proposal is mainly devised for investors, academic heads, or other people who would value the legitimacy of your project proposal and assess the merit. For successful research proposals, you need to follow certain guidelines for efficiency. We have all sorts of medical research proposal templates for users with specific needs. Whether it’s for a clinical Ph.D. research on public health issues or an undergraduate letter over pharmacology, we got them all right here. Or if you are wondering, you can use proposal templates that bring ready-made documents for your benefit!

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• Published: 18 April 2024

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
• Armando Guio Español 7 ,
• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
• Daudi Jjingo 10 ,
• Katherine Littler 9 ,
• Daniela Paolotti 11 &
• Effy Vayena 12  

BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

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Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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DOI : https://doi.org/10.1186/s12910-024-01044-w

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Our mission is protecting the public from deceptive or unfair business practices and from unfair methods of competition through law enforcement, advocacy, research, and education.

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Lina M. Khan was sworn in as Chair of the Federal Trade Commission on June 15, 2021.

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FTC Announces Rule Banning Noncompetes

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Today, the Federal Trade Commission issued a final rule to promote competition by banning noncompetes nationwide, protecting the fundamental freedom of workers to change jobs, increasing innovation, and fostering new business formation.

“Noncompete clauses keep wages low, suppress new ideas, and rob the American economy of dynamism, including from the more than 8,500 new startups that would be created a year once noncompetes are banned,” said FTC Chair Lina M. Khan. “The FTC’s final rule to ban noncompetes will ensure Americans have the freedom to pursue a new job, start a new business, or bring a new idea to market.”

The FTC estimates that the final rule banning noncompetes will lead to new business formation growing by 2.7% per year, resulting in more than 8,500 additional new businesses created each year. The final rule is expected to result in higher earnings for workers, with estimated earnings increasing for the average worker by an additional $524 per year, and it is expected to lower health care costs by up to $194 billion over the next decade. In addition, the final rule is expected to help drive innovation, leading to an estimated average increase of 17,000 to 29,000 more patents each year for the next 10 years under the final rule.

Noncompetes are a widespread and often exploitative practice imposing contractual conditions that prevent workers from taking a new job or starting a new business. Noncompetes often force workers to either stay in a job they want to leave or bear other significant harms and costs, such as being forced to switch to a lower-paying field, being forced to relocate, being forced to leave the workforce altogether, or being forced to defend against expensive litigation. An estimated 30 million workers—nearly one in five Americans—are subject to a noncompete.

Under the FTC’s new rule, existing noncompetes for the vast majority of workers will no longer be enforceable after the rule’s effective date. Existing noncompetes for senior executives - who represent less than 0.75% of workers - can remain in force under the FTC’s final rule, but employers are banned from entering into or attempting to enforce any new noncompetes, even if they involve senior executives. Employers will be required to provide notice to workers other than senior executives who are bound by an existing noncompete that they will not be enforcing any noncompetes against them.

In January 2023, the FTC issued a  proposed rule which was subject to a 90-day public comment period. The FTC received more than 26,000 comments on the proposed rule, with over 25,000 comments in support of the FTC’s proposed ban on noncompetes. The comments informed the FTC’s final rulemaking process, with the FTC carefully reviewing each comment and making changes to the proposed rule in response to the public’s feedback.

In the final rule, the Commission has determined that it is an unfair method of competition, and therefore a violation of Section 5 of the FTC Act, for employers to enter into noncompetes with workers and to enforce certain noncompetes.

The Commission found that noncompetes tend to negatively affect competitive conditions in labor markets by inhibiting efficient matching between workers and employers. The Commission also found that noncompetes tend to negatively affect competitive conditions in product and service markets, inhibiting new business formation and innovation. There is also evidence that noncompetes lead to increased market concentration and higher prices for consumers.

Alternatives to Noncompetes

The Commission found that employers have several alternatives to noncompetes that still enable firms to protect their investments without having to enforce a noncompete.

Trade secret laws and non-disclosure agreements (NDAs) both provide employers with well-established means to protect proprietary and other sensitive information. Researchers estimate that over 95% of workers with a noncompete already have an NDA.

The Commission also finds that instead of using noncompetes to lock in workers, employers that wish to retain employees can compete on the merits for the worker’s labor services by improving wages and working conditions.

Changes from the NPRM

Under the final rule, existing noncompetes for senior executives can remain in force. Employers, however, are prohibited from entering into or enforcing new noncompetes with senior executives. The final rule defines senior executives as workers earning more than $151,164 annually and who are in policy-making positions.

Additionally, the Commission has eliminated a provision in the proposed rule that would have required employers to legally modify existing noncompetes by formally rescinding them. That change will help to streamline compliance.

Instead, under the final rule, employers will simply have to provide notice to workers bound to an existing noncompete that the noncompete agreement will not be enforced against them in the future. To aid employers’ compliance with this requirement, the Commission has included model language in the final rule that employers can use to communicate to workers. 

The Commission vote to approve the issuance of the final rule was 3-2 with Commissioners Melissa Holyoak and Andrew N. Ferguson voting no. Commissioners Rebecca Kelly Slaughter , Alvaro Bedoya , Melissa Holyoak and Andrew N. Ferguson each issued separate statements. Chair Lina M. Khan will issue a separate statement.

The final rule will become effective 120 days after publication in the Federal Register.

Once the rule is effective, market participants can report information about a suspected violation of the rule to the Bureau of Competition by emailing  [email protected] . 

The Federal Trade Commission develops policy initiatives on issues that affect competition, consumers, and the U.S. economy. The FTC will never demand money, make threats, tell you to transfer money, or promise you a prize. Follow the  FTC on social media , read  consumer alerts  and the  business blog , and  sign up to get the latest FTC news and alerts .

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What marijuana reclassification means for the United States

The U.S. Drug Enforcement Administration will move to reclassify marijuana as a less dangerous drug, a historic shift to generations of American drug policy that could have wide ripple effects across the country.

FILE - Marijuana plants are seen at a secured growing facility in Washington County, N.Y., May 12, 2023. The U.S. Drug Enforcement Administration will move to reclassify marijuana as a less dangerous drug, a historic shift to generations of American drug policy that could have wide ripple effects across the country. (AP Photo/Hans Pennink, File)

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Budtender Rey Cruz weighs cannabis for a customer at the Marijuana Paradise on Friday, April 19, 2024, in Portland, Ore. (AP Photo/Jenny Kane)

Cloud 9 Cannabis employee Beau McQueen, right, helps a customer, Saturday, April 13, 2024, in Arlington, Wash. The shop is one of the first dispensaries to open under the Washington Liquor and Cannabis Board’s social equity program, established in efforts to remedy some of the disproportionate effects marijuana prohibition had on communities of color. (AP Photo/Lindsey Wasson)

WASHINGTON (AP) — The U.S. Drug Enforcement Administration is moving toward reclassifying marijuana as a less dangerous drug. The Justice Department proposal would recognize the medical uses of cannabis , but wouldn’t legalize it for recreational use.

The proposal would move marijuana from the “Schedule I” group to the less tightly regulated “Schedule III.”

So what does that mean, and what are the implications?

WHAT HAS ACTUALLY CHANGED? WHAT HAPPENS NEXT?

Technically, nothing yet. The proposal must be reviewed by the White House Office of Management and Budget, and then undergo a public-comment period and review from an administrative judge, a potentially lengthy process.

Still, the switch is considered “paradigm-shifting, and it’s very exciting,” Vince Sliwoski, a Portland, Oregon-based cannabis and psychedelics attorney who runs well-known legal blogs on those topics, told The Associated Press when the federal Health and Human Services Department recommended the change.

“I can’t emphasize enough how big of news it is,” he said.

It came after President Joe Biden asked both HHS and the attorney general, who oversees the DEA, last year to review how marijuana was classified. Schedule I put it on par, legally, with heroin, LSD, quaaludes and ecstasy, among others.

Biden, a Democrat, supports legalizing medical marijuana for use “where appropriate, consistent with medical and scientific evidence,” White House press secretary Karine Jean-Pierre said Thursday. “That is why it is important for this independent review to go through.”

Cloud 9 Cannabis employee Beau McQueen, right, helps a customer, Saturday, April 13, 2024, in Arlington, Wash. (AP Photo/Lindsey Wasson)

IF MARIJUANA GETS RECLASSIFIED, WOULD IT LEGALIZE RECREATIONAL CANNABIS NATIONWIDE?

Ap audio: what marijuana reclassification means for the united states.

AP correspondent Haya Panjwani reports on a proposal for the federal government to reclassify marijuana in what would be a historic shift that could have wide ripple effects across the country.

No. Schedule III drugs — which include ketamine, anabolic steroids and some acetaminophen-codeine combinations — are still controlled substances.

They’re subject to various rules that allow for some medical uses, and for federal criminal prosecution of anyone who traffics in the drugs without permission.

No changes are expected to the medical marijuana programs now licensed in 38 states or the legal recreational cannabis markets in 23 states, but it’s unlikely they would meet the federal production, record-keeping, prescribing and other requirements for Schedule III drugs.

There haven’t been many federal prosecutions for simply possessing marijuana in recent years, even under marijuana’s current Schedule I status, but the reclassification wouldn’t have an immediate impact on people already in the criminal justice system.

“Put simple, this move from Schedule I to Schedule III is not getting people out of jail,” said David Culver, senior vice president of public affairs at the U.S. Cannabis Council.

But rescheduling in itself would have some impact, particularly on research and marijuana business taxes.

WHAT WOULD THIS MEAN FOR RESEARCH?

Because marijuana is on Schedule I, it’s been very difficult to conduct authorized clinical studies that involve administering the drug. That has created something of a Catch-22: calls for more research, but barriers to doing it. (Scientists sometimes rely instead on people’s own reports of their marijuana use.)

Marijuana plants are seen at a secured growing facility in Washington County, N.Y., May 12, 2023. (AP Photo/Hans Pennink, File)

Schedule III drugs are easier to study, though the reclassification wouldn’t immediately reverse all barriers to study.

“It’s going to be really confusing for a long time,” said Ziva Cooper, director of the University of California, Los Angeles Center for Cannabis and Cannabinoids. “When the dust has settled, I don’t know how many years from now, research will be easier.”

Among the unknowns: whether researchers will be able to study marijuana from state-licensed dispensaries and how the federal Food and Drug Administration might oversee that.

Some researchers are optimistic.

“Reducing the schedule to schedule 3 will open up the door for us to be able to conduct research with human subjects with cannabis,” said Susan Ferguson, director of University of Washington’s Addictions, Drug & Alcohol Institute in Seattle.

WHAT ABOUT TAXES (AND BANKING)?

Under the federal tax code, businesses involved in “trafficking” in marijuana or any other Schedule I or II drug can’t deduct rent, payroll or various other expenses that other businesses can write off. (Yes, at least some cannabis businesses, particularly state-licensed ones, do pay taxes to the federal government, despite its prohibition on marijuana.) Industry groups say the tax rate often ends up at 70% or more.

The deduction rule doesn’t apply to Schedule III drugs, so the proposed change would cut cannabis companies’ taxes substantially.

They say it would treat them like other industries and help them compete against illegal competitors that are frustrating licensees and officials in places such as New York .

“You’re going to make these state-legal programs stronger,” says Adam Goers, of The Cannabist Company, formerly Columbia Care. He co-chairs a coalition of corporate and other players that’s pushing for rescheduling.

It could also mean more cannabis promotion and advertising if those costs could be deducted, according to Beau Kilmer, co-director of the RAND Drug Policy Center.

Rescheduling wouldn’t directly affect another marijuana business problem: difficulty accessing banks, particularly for loans, because the federally regulated institutions are wary of the drug’s legal status. The industry has been looking instead to a measure called the SAFE Banking Act . It has repeatedly passed the House but stalled in the Senate.

ARE THERE CRITICS? WHAT DO THEY SAY?

Indeed, there are, including the national anti-legalization group Smart Approaches to Marijuana. President Kevin Sabet, a former Obama administration drug policy official, said the HHS recommendation “flies in the face of science, reeks of politics” and gives a regrettable nod to an industry “desperately looking for legitimacy.”

Some legalization advocates say rescheduling weed is too incremental. They want to keep the focus on removing it completely from the controlled substances list, which doesn’t include such items as alcohol or tobacco (they’re regulated, but that’s not the same).

Paul Armentano, the deputy director of the National Organization for the Reform of Marijuana Laws, said that simply reclassifying marijuana would be “perpetuating the existing divide between state and federal marijuana policies.” Kaliko Castille, a past president of the Minority Cannabis Business Association, said rescheduling just “re-brands prohibition,” rather than giving an all-clear to state licensees and putting a definitive close to decades of arrests that disproportionately pulled in people of color.

“Schedule III is going to leave it in this kind of amorphous, mucky middle where people are not going to understand the danger of it still being federally illegal,” he said.

This story has been corrected to show that Kaliko Castille is a past president, not president, of the Minority Cannabis Business Association and that Columbia Care is now The Cannabist Company.

___ Peltz reported from New York. Associated Press writers Colleen Long in Washington and Carla K. Johnson in Seattle contributed to this report.