6 Ethical Considerations for Clinical Research Involving AI: What Sponsors and CROs Need to Know

Artificial intelligence (AI) is opening new possibilities in the evolution of medicine through the development of new tools and devices to assist with patient care. For sponsors and CROs, that opportunity comes with a growing tension: the same technologies that can accelerate medical care and improve decision-making also raise a more complex set of ethical questions that aren’t always easy to anticipate or manage.  

To help address these questions, WCG, in collaboration with the MRCT Center and a multi-stakeholder group spanning bioethics, clinical research, regulatory science, and AI technology, developed the Framework for Review of Clinical Research Involving AI. This framework provides IRBs, sponsors, investigators, and research institutions with a practical, structured approach to the ethical oversight of AI when it is being studied as a clinical intervention through clinical research.  

For sponsors and CROs, these challenges are not theoretical. They shape protocol design, participant protections, data governance strategies, and review readiness across the study lifecycle. Addressing them early can help reduce avoidable delays, support more consistent IRB review, and strengthen confidence in study outcomes. In this context, clinical research involving AI as the intervention requires deliberation of the following ethical considerations during IRB review. 

6 Ethical Considerations for Clinical Research Involving AI 

1. Human Judgment Must Remain Central 

AI may support better, faster decisions, but it should not displace human judgment that affects diagnosis, treatment, disease prevention, or participant well-being. Researchers and clinicians must remain central to decision-making, with clear safeguards to prevent over-reliance on AI outputs or deference to algorithmic recommendations.  

For sponsors and CROs, that means thinking beyond whether a model performs well. It means defining who reviews research outputs, when human intervention is required, how risks are assessed, and what study teams in training need to understand about the technology’s limitations before it influences research or care.  

2. AI Must Be Proven Safe Before It’s Trusted 

Before AI is used with study participants, it should demonstrate that it is reliable enough for the role it is expected to play. Best practices include validation testing before deployment, performance monitoring during the study, and stronger oversight when algorithms continue to learn or adapt over time. If an AI system changes in ways that affect participant risk, that may warrant additional review. 

This makes technical robustness more than a model-development issue; it becomes a study oversight issue. Sponsors and CROs should be prepared to show how limitations are understood, how unexpected outputs or adverse events will be handled, and how ongoing performance will be monitored, especially if the system is adaptive. 

3. Data Responsibility Doesn’t End at Collection 

Clinical research depends on data, often at a scale that can intensify privacy and confidentiality concerns. The need for strong data governance when AI is involved – including appropriate safeguards, documentation of training data and performance, and clear plans for retention and deletion – is paramount. It also highlights a growing reality: even when data is de-identified, it may still be linkable in ways that create re-identification risk. 

That has real implications for sponsors and CROs working across multiple data sources and study systems. Governance needs to cover not only how data is protected but also how its provenance, traceability, and intended use are documented clearly enough to support ethical review and preserve participant trust. 

4. Trust Depends on Transparency 

Transparency is closely tied to explainability and interpretability. Reviewers, researchers, clinicians, and participants should be able to understand what the AI is meant to do, what its limitations are, and how it produces outputs that could influence care. This is especially important when black-box systems limit visibility into how conclusions are reached.  

For sponsors and CROs, transparency is not just a communications exercise. It is part of how risk is understood and how trust is built. Teams should understand the intended use of the AI, what information will be shared with participants, and how potential biases or errors will be identified and addressed. 

5. Fair Outcomes Start with Representative Data 

AI can only support fair research decisions if the data behind it is fit for purpose. IRBs should consider whether datasets are sufficiently representative of the population the AI will affect, whether underrepresented groups may be excluded or disproportionately impacted, and whether issues such as access, connectivity, or digital literacy could create inequities in who can participate in the research or benefit from the intervention.  

This raises a practical question early in study planning: does the data reflect the real-world population the AI is meant to serve? If not, bias is not just a model problem. It can impact the AI output and, ultimately, the reliability of the AI intervention when deployed clinically across diverse individuals and populations.  

6. Participants Must Understand AI’s Role 

The informed consent discussion must clearly outline the role of AI as the intervention in the research. Plain-language explanations of how the AI functions in the research – how participant data will be used, what privacy or confidentiality risks may exist, and whether participants truly understand those implications before enrolling – are critical. Consent language should help participants understand how AI changes the nature of data use, oversight, and potential risk. Discussions should include expectations around the secondary and future use of participant data, and whether data incorporated into an AI system can realistically be removed later.  
 

Ethical AI Starts with Ethical Research 

The ethical questions raised by AI are often framed as entirely new challenges. In reality, many trace back to familiar research principles: protecting participants, minimizing risk, promoting fairness, supporting informed decision-making, and maintaining appropriate oversight. The framework helps translate those principles into practical review considerations for studies where AI acts as the intervention in the research.  

For sponsors and CROs, the value of this lens is practical as much as ethical. Addressing these issues earlier can help teams surface avoidable risks, strengthen participant protections, improve the quality and clarity of protocol materials, and support a more thoughtful review process. As AI takes on a larger role in clinical care and is studied in clinical research, utilizing the ethical concepts outlined in the Framework for Review of Clinical Research Involving AI will help sponsors and CROs ensure that the research meets appropriate criteria for participant protection and IRB review.