Member May 2026: Preserving Safety, Trust, and Human Judgment in the Age of Clinical AI (On-Demand)-Session Recording

Video Transcription

Video Summary

This Member May session from AAPMR explored “Preserving Safety, Trust, and Human Judgment in the Age of Clinical AI.” Moderated by Dr. Moo-Yeon Oh Park, the panel featured experts from clinical practice, digital health, law, and medical technology.<br /><br />The discussion focused on how AI is already being used in healthcare, especially ambient scribes and documentation tools like DAX, which many participants said save time and reduce burnout. However, panelists emphasized that AI success should not be measured only by efficiency or productivity. Instead, it should be judged by whether it improves patient care, preserves clinician-patient relationships, and supports human judgment rather than replacing it.<br /><br />A major theme was the risk that efficiencies gained from AI could be used by health systems to push clinicians to see more patients, rather than spend more time with each one. The group also discussed the importance of “whole-person” data, including social determinants of health, wearable data, environmental context, and family factors, while recognizing major concerns about privacy, bias, missing data, and who owns or controls that information.<br /><br />Legal and regulatory issues were another key topic. Renee Kwasi explained that accountability for AI-related errors is still largely unsettled, and liability may eventually extend to clinicians, developers, and deployers. Tim Su highlighted the need for voluntary standards and clinician involvement in governance and AI design.<br /><br />The session ended with a call for advocacy, collaboration, and clinician participation in shaping AI standards so that technology remains a tool for better, safer, more human care.

Keywords

clinical AI

ambient scribes

DAX documentation

patient care

clinician-patient relationship

human judgment

healthcare efficiency

whole-person data

social determinants of health

AI liability

AI governance