Self-regulating the use of large language models in clinical practice: a risk-stratified approach

Abstract

The rapid integration of large language models (LLMs) into clinical practice offers promising benefits, including assistance with documentation, decision support and patient communication. However, these advantages are tempered by concerns around model accuracy, data privacy, clinician trust and regulatory responsibility. In this comment, we propose a clinician-led, risk-stratified framework to guide responsible adoption of LLMs in healthcare. The framework categorises applications into four risk tiers: low risk, moderate risk, high risk and critical risk. Each tier demands tailored oversight, validation and governance, with increasing levels of statutory regulatory scrutiny (eg., European Union Artificial Intelligence Act and U.S. Food and Drug Administration) and clinical supervision. We argue that proactive self-regulation combined with ongoing quality management and clinician education is essential to safely integrate LLMs into ongoing care.

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