ChatGPT's learning and reasoning capacity in anesthesiology

Keywords: ChatGPT, Artificial intelligence, Anesthesiology, Difficult airway, Learning, Reasoning, Decision-making


Introduction: Over the past few months, ChatGPT has raised a lot of interest given its ability to perform complex tasks through natural language and conversation. However, its use in clinical decision-making is limited and its application in the field of anesthesiology is unknown.

Objective: To assess ChatGPT’s basic and clinical reasoning and its learning ability in a performance test on general and specific anesthesia topics.

Methods: A three-phase assessment was conducted. Basic knowledge of anesthesia was assessed in the first phase, followed by a review of difficult airway management and, finally, measurement of decision-making ability in ten clinical cases. The second and the third phases were conducted before and after feeding ChatGPT with the 2022 guidelines of the American Society of Anesthesiologists on difficult airway management.

Results: On average, ChatGPT succeded 65% of the time in the first phase and 48% of the time in the second phase. Agreement in clinical cases was 20%, with 90% relevance and 10% error rate. After learning, ChatGPT improved in the second phase, and was correct 59% of the time, with agreement in clinical cases also increasing to 40%.

Conclusions: ChatGPT showed acceptable accuracy in the basic knowledge test, high relevance in the management of specific difficult airway clinical cases, and the ability to improve after learning.


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How to Cite
Cruz G, Pedroza S, Ariza F. ChatGPT’s learning and reasoning capacity in anesthesiology. Colomb. J. Anesthesiol. [Internet]. 2023 Nov. 17 [cited 2024 Jul. 25];52(1). Available from:


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How to Cite
Cruz G, Pedroza S, Ariza F. ChatGPT’s learning and reasoning capacity in anesthesiology. Colomb. J. Anesthesiol. [Internet]. 2023 Nov. 17 [cited 2024 Jul. 25];52(1). Available from:


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