Liability of AI in Medicine: Recent Insights
- Automation Bias Risk: AI-driven clinical decision support may lead to over-reliance, increasing the risk of diagnostic errors (Khera et al., JAMA 2023).
Rationale: Clinicians may trust AI outputs without critical review, compromising patient safety.
- Accountability Challenges: Unclear responsibility when AI contributes to harm (Hernán & Robins, Causal Inference 2020).
Rationale: Determining liability (developer vs. clinician) remains legally ambiguous.
- Regulatory Gaps: Current frameworks (e.g., FDA, EU AI Act) lag behind rapid AI adoption (Mandelblatt et al., Nat Med 2024).
Rationale: Insufficient oversight may delay addressing AI-specific risks.
- Collaboration Needed: Clinician-AI developer partnerships are crucial to balance innovation with safety (Mandelblatt et al., Nat Med 2024).
Rationale: Joint efforts can mitigate risks like misdiagnosis or treatment delays.
Key Takeaway: AI in medicine offers benefits but requires robust governance to minimize liability risks.