Artificial intelligence-related health research: ethics review and oversight
This report examines ethics review and oversight of AI-related health research, covering health-related data science using AI, research conducted with AI tools, and research on AI tools themselves. It analyses gaps in current ethical standards, challenges faced by research ethics committees, and measures to strengthen ethical research conduct. Particular attention is given to risks and inequities affecting low- and middle-income countries, including fairness, benefit sharing, data colonialism, and capacity-building.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This report is for researchers, research ethics committees, funders, publishers, and policymakers involved in health-related research involving artificial intelligence.
What the document contains
The document addresses ethics review and oversight mechanisms for AI-related health research, identifies gaps in current ethical standards, examines challenges faced by research ethics committees, and discusses considerations for strengthening responsible research conduct.
Binding legal regulations or specific enforcement mechanisms are not established in this report.
- Artificial intelligence is increasingly used in health-related research, creating novel risks that existing oversight systems may not fully address.
- The report examines ethics review and oversight across three categories of AI-related health research, including data science, research conducted with AI tools, and research on AI tools.
- Particular attention is given to risks and inequities affecting low- and middle-income countries such as fairness, data colonialism, and ethics dumping.
- The report explores complementary roles for funders, data access mechanisms, scientific journals, publishers, and regulatory agencies in strengthening research oversight.
Not stated
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
1/5Clear responsibility for AI outcomes
measures to strengthen researchers’ adoption of ethical principles
Overview
screening score · 2026-10-23
Fairness
2/5Non-discrimination and equitable treatment
Particular attention is given to risks and inequities affecting low- and middle-income countries, including fairness
Overview
screening score · 2026-10-23
Human Dignity
1/5Respect for the inherent worth of persons
challenge established ethical principles and human rights protections
Overview
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
Overview
screening score · 2026-10-23
Privacy
2/5Protection of personal data and private life
health-related data science using AI
Overview
screening score · 2026-10-23
Transparency
0/5Openness about how AI systems work and are used
Overview
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
Overview
screening score · 2026-10-23
Impact Assessment
2/5Required assessment of an AI system's risks/impacts before or during use
create novel risks that existing oversight systems do not fully address
Overview
screening score · 2026-10-23