Ethical Considerations in Autonomous AI Clinical Research: Insights from a Delphi Study
By
Ariadne A Nichol
Alaa Youssef
David B Larson
Michael Abramoff
Risa M Wolf
Danton Char
Nicole Martinez-Martin
August 21, 2026
Clinical Scorecard: Ethical Considerations in Autonomous AI Clinical Research: Insights from a Delphi Study
At a Glance
Category Detail
Condition Autonomous AI in Clinical Research Key Mechanisms Ethical principles governing medical research applied to AI tools Target Population Patients undergoing screening for diseases using AI tools Care Setting Clinical research trials evaluating autonomous AI technologies
Key Highlights
Rapid increase in AI medical devices authorized by the FDA since 2018 Need for explicit guidance on ethical principles for AI clinical evaluation Concerns about bias and population-level performance differences in AI tools Emanuel et al's 7 core ethical principles for clinical research Challenges in applying ethical principles due to stakeholder effects and trade-offs Guideline-Based Recommendations
Diagnosis
Evaluate AI tools for clinical relevance and bias mitigation Management
Incorporate stakeholder values in AI design and deployment Monitoring & Follow-up
Assess the performance of AI tools across diverse populations Risks
Address potential trade-offs between algorithm performance and patient safety Patient & Prescribing Data
Individuals screened for diabetic retinopathy and other diseases
AI tools must be evaluated for their impact on various demographic groups
Clinical Best Practices
Adhere to ethical principles endorsed by the NIH in AI clinical research Ensure transparency and accountability in AI tool development Conduct independent reviews of AI-based clinical trials Related Resources & Content
