AI Ethics Guidelines - Healthcare Edition

Overview

Healthcare AI ethics builds on centuries of medical ethics tradition—primum non nocere, patient autonomy, beneficence, justice—while addressing challenges Hippocrates never imagined. What does informed consent mean when AI assists diagnosis? How do we ensure AI doesn't perpetuate health disparities? When should clinicians override AI recommendations? These questions require healthcare-specific ethical frameworks.

This comprehensive guide provides ethical guidelines specifically designed for clinical AI. It grounds AI ethics in healthcare traditions while providing practical guidance for the novel situations AI creates.

What's Inside

• Why Healthcare AI Ethics Is Different: The unique ethical stakes when AI affects patient health, safety, and life
• Ethical Principles for Healthcare AI: Beneficence, non-maleficence, autonomy, justice, and dignity applied to clinical AI
• Clinical AI Ethics: Diagnostic AI ethics (false positives/negatives, uncertainty communication), treatment recommendation ethics (evidence integration, preference alignment), and clinical workflow ethics
• Bias and Health Equity: Identifying and mitigating algorithmic bias, ensuring AI doesn't perpetuate health disparities, and serving all patient populations equitably
• Patient Autonomy and Consent: Informed consent for AI-assisted care, patient rights regarding AI decisions, transparency about AI use, and respecting patient preferences
• Clinician-AI Relationship: Appropriate trust calibration, when clinicians should override AI, maintaining clinical judgment, and avoiding automation bias
• End-of-Life and High-Stakes Decisions: Ethical use of AI in prognosis, treatment withdrawal decisions, resource allocation, and palliative care
• Research Ethics for Healthcare AI: Training data ethics, clinical trial considerations, publication ethics, and community benefit
• Clinical Ethics Review Framework: When ethics review is required, committee composition, review process, and documentation
• Ethics Training Program: Building ethical awareness among clinicians, AI developers, and healthcare administrators
• Case Studies: Real healthcare AI ethical dilemmas with analysis and lessons learned

Who This Is For

• Chief Medical Information Officers responsible for clinical AI governance
• Clinical Ethics Committees reviewing AI implementations
• Patient Safety Officers ensuring AI doesn't harm patients
• Health Equity Officers addressing algorithmic bias
• Clinical AI Teams developing and deploying healthcare AI

Why This Resource

Healthcare AI ethics requires healthcare-specific guidance. Generic AI ethics principles don't address informed consent for AI-assisted diagnosis, the ethics of AI in end-of-life decisions, or how to ensure AI serves all patient populations equitably. This framework addresses healthcare's unique ethical landscape.

Grounding in medical ethics traditions means the framework builds on principles healthcare organizations already understand—extending them to AI rather than imposing foreign frameworks.

FAQ

Q: How should patients be informed about AI use in their care?

A: The patient autonomy section covers informed consent for AI: what patients should know, when and how to disclose AI use, and respecting patients who prefer human-only care. It provides practical guidance while respecting different organizational approaches.

Q: What about AI bias in clinical settings?

A: Bias and health equity is a dedicated section covering how to identify algorithmic bias in healthcare AI, methodologies for testing across patient populations, and approaches to ensuring AI benefits all patients equitably.

Q: How do we handle disagreement between AI and clinicians?

A: The clinician-AI relationship section addresses this: appropriate trust calibration, when clinicians should override AI recommendations, how to document overrides, and avoiding both automation bias (over-trusting AI) and automation disuse (ignoring AI).