A Patient Simulation Framework for Risk Assessment of Conversational Healthcare AI: Evaluation of an Antidepressant Decision Aid
arXiv:2602.11391v4 Announce Type: replace Abstract: Objective: This study develops and validates a patient simulation framework that aligns with the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) MAP and MEASURE functions, providing an empirical basis for identifying and characterizing performance risks in conversational clinical AI across medical, linguistic, and behavioral patient variation. We applied the framework to a conversational decision aid for antidepressant selection in major depressive disorder (the AI Decision Aid). Methods: The simula
The increasing deployment of conversational AI in sensitive sectors like healthcare necessitates robust and standardized risk assessment frameworks, aligning with growing regulatory emphasis on AI safety.
This study introduces a systematic approach to evaluating the performance risks of healthcare AI, which is crucial for building trust, ensuring patient safety, and enabling responsible AI adoption at scale.
The development and validation of this simulation framework provide a structured method for identifying and mitigating risks in conversational clinical AI, potentially influencing future regulatory compliance and development practices.
- · Healthcare AI developers
- · AI ethicists and regulators
- · Patients receiving AI-assisted care
- · AI risk management platform providers
- · AI developers ignoring safety protocols
- · Healthcare systems with inadequate AI governance
- · Patients harmed by unvalidated AI
Improved safety and reliability of conversational healthcare AI.
Accelerated adoption of AI in clinical settings due to increased trust and reduced regulatory hurdles.
Standardization of AI risk assessment frameworks beyond healthcare, influencing other high-stakes applications.
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