aceA Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study
Background Conversational AI systems are increasingly being deployed in health care for clinical decision support, but their performance varies substantially across patient communication styles, health literacy levels, and behavioral patterns. Static benchmarks cannot capture multiturn dynamics through which this variation compounds, and no current evaluation framework implements structured AI risk management guidance for conversational health care AI. The result is a structural risk: AI systems may perform well…
A simulation framework aligned with the NIST AI RMF exposes performance risks in conversational clinical AI before deployment, with simulated patients expressing medical concepts at high fidelity.
The antidepressant decision aid degraded steadily as health literacy fell, retrieving the top concept correctly 47.6% of the time for limited literacy versus 81.9% for proficient literacy.
The framework was evaluated only on antidepressant selection, so its usefulness for other clinical decision-aid tasks has not been shown.
Evidence
- Peer-reviewedJMIR AI2026-10-05
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Truvace Impact Record TRV-2026-1864, v1: “A Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study.” Truvace, 2026-10-18. /record/TRV-2026-1864 (accessed at citation time). sha256 778278fdd05abc66…
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