Using AI chatbots as clinical decision support for regional anesthesia: is the new era of “e-Consults” ready for primetime?

Abstract

BACKGROUND AND OBJECTIVE: There is growing interest to further incorporate artificial intelligence into clinical practice. Perioperative decision-making for patients considered for regional anesthesia is complex, and the use of artificial intelligence as a clinical decision support tool in this context is underinvestigated. We aim to evaluate large language models (LLMs) when prompted with clinical scenarios and management questions in the subspecialty of regional anesthesiology. METHODS: A pilot study was conducted with four popular LLM-based chatbots. They were prompted with the same basic and complex clinical scenarios and common management questions followed by a qualitative review specific to regional anesthesiology. RESULTS: All four platforms demonstrated weaknesses in clinical judgment, thoroughness, and rationale in decision-making that were more pronounced for the complex scenario. There is evidence of confabulations in the responses. CONCLUSIONS: LLM-based chatbots applied to the subspecialty of regional anesthesiology are not ready to truly assist in basic or complex clinical scenarios.

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