RAISE 2025 panel statement on aligning AI to clinical values

2025-10-04 · Source: David Stutz · Field: Health & Wellbeing — Medical Devices & Health Technology, Health & Medical Research, AI in Healthcare · Depth: Advanced, short

Summary

A September 2025 RAISE symposium panel discussed aligning AI to clinical values, particularly in healthcare. The author, a panelist, highlighted that while health AI often aligns with clinicians and healthcare systems (e.g., workflows, billing), aligning with patients is significantly more challenging. This difficulty stems from regulatory barriers preventing direct patient interaction, information asymmetry where patients may struggle to articulate all values, and the inherent diversity ("pluralistic alignment") among patients compared to clinicians. The author advocates for prioritizing patient values, focusing on the "patient's best interest" which encompasses preferences like empathy and involvement, alongside long-term well-being. The discussion also drew parallels with general AI safety research, suggesting that healthcare AI can adopt risk frameworks and acknowledge complex user-AI relationships from that field.

Key takeaway

For research scientists developing healthcare AI, you should prioritize aligning systems with patient values, recognizing the challenges of patient diversity and information asymmetry. Focus on the "patient's best interest" as a guiding principle, encompassing both explicit preferences and long-term well-being. Consider adapting established AI safety risk frameworks to systematically address and mitigate misalignment risks in patient-facing applications.

Key insights

Aligning healthcare AI with diverse patient values is critical yet complex, requiring a focus on "patient's best interest."

Principles

• Healthcare AI should prioritize patient values.
• Patient values are diverse and complex to ascertain.
• AI safety frameworks can inform health AI alignment.

Method

Aligning healthcare AI involves considering the Quadruple Aim, prioritizing patient values over clinician/system values, and defining "patient's best interest" to include preferences and long-term well-being.

In practice

• Collaborate with patient actors for feedback.
• Adopt AI safety risk frameworks for health AI.
• Acknowledge complex patient-AI relationships.

Topics

• AI Alignment
• Healthcare AI Ethics
• Patient Values
• AI Safety Frameworks
• Medical AI Applications

Best for: Research Scientist, AI Researcher, AI Scientist, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by David Stutz.