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Large Language Models (LLMs) have emerged as auxiliary tools in clinical decision-making. However, their inherent opacity raises concerns about reliability and interpretability in medical practice. This study evaluates the reasoning structure of different LLMs in generating clinical recommendations, focusing on logical coherence and adherence to medical guidelines. Six complex clinical cases were submitted to three LLMs: OpenAI ChatGPT-4o, OpenAI O3-Mini-High, and Gemini 2 Flash Thinking Experimental. Model-generated responses were analyzed using Bardin’s content analysis and statistically compared to reference guidelines. Results indicate that, while all models exhibit structured reasoning, variations exist in the depth of clinical considerations, particularly in postoperative care and diagnostic test selection. The integration of explicit reasoning frameworks enhances traceability in AI-generated medical recommendations. However, further refinements are required to ensure consistency and reliability in clinical applications.

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Reasoning Models and Decision-Making in Medicine—Are we Finally There?

  • 会议论文

  • 发表日期：

    2026年

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  • Gerson Hiroshi Yoshinari Junior,
  • Ana Lídia Corrêa da Silva Moreira,
  • Samantha Teofilo Valerio Yoshinari,
  • Luciano Magalhães Vitorino

摘要

Large Language Models (LLMs) have emerged as auxiliary tools in clinical decision-making. However, their inherent opacity raises concerns about reliability and interpretability in medical practice. This study evaluates the reasoning structure of different LLMs in generating clinical recommendations, focusing on logical coherence and adherence to medical guidelines. Six complex clinical cases were submitted to three LLMs: OpenAI ChatGPT-4o, OpenAI O3-Mini-High, and Gemini 2 Flash Thinking Experimental. Model-generated responses were analyzed using Bardin’s content analysis and statistically compared to reference guidelines. Results indicate that, while all models exhibit structured reasoning, variations exist in the depth of clinical considerations, particularly in postoperative care and diagnostic test selection. The integration of explicit reasoning frameworks enhances traceability in AI-generated medical recommendations. However, further refinements are required to ensure consistency and reliability in clinical applications.