Large language models (LLMs) like ChatGPT often exhibit Type 1 thinking—fast, intuitive reasoning that relies on familiar patterns—which can be dangerously simplistic in complex medical or ethical scenarios requiring more deliberate analysis. In our recent explorations, we observed that LLMs frequently default to well-known answers, failing to recognize nuances or twists in presented situations. For instance, when faced with modified versions of the classic “Surgeon’s Dilemma” or medical ethics cases where typical dilemmas were resolved, LLMs still reverted to standard responses, overlooking critical details. Even models designed for enhanced analytical reasoning, such as ChatGPT-o1, did not consistently overcome these limitations. This suggests that despite advancements toward fostering Type 2 thinking, LLMs remain heavily influenced by familiar patterns ingrained during training. As LLMs are increasingly integrated into clinical practice, it is crucial to acknowledge and address these shortcomings to ensure reliable and contextually appropriate AI assistance in medical decision-making.

The authors have declared no competing interest.

This study did not receive any funding

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