As physicians turn to AI-powered systems to help meet the dual demands of speed and care quality, they are met with hallucinations and sycophancy. Understanding how doctors reason through clinical problems in real-world settings is critical for design of effective AI reasoning systems. While recent advances in medical AI have emphasized performance benchmarks and diagnostic accuracy, comparatively little attention has been paid to the structure of clinicians' reasoning processes as they unfold over time, e.g., how they interact with electronic health records and operate under conditions of uncertainty and constraint. This study provides a comprehensive, empirically-grounded account of clinical reasoning and its relationship to current AI-mediated workflows through a mixed-methods design that combines qualitative interviews with structured survey data. Findings indicate that current AI systems are primarily deployed for encounter-level tasks such as documentation and summarization, and only partially align with physicians' underlying reasoning processes. In particular, AI-generated representations often omit temporal or interpretive structures central to clinical decision-making, while core aspects of reasoning, especially those spanning multiple encounters, remain largely implicit and physician-driven. By integrating fine-grained qualitative insights with broader quantitative patterns, this study offers a unified framework for understanding clinical reasoning as a context-sensitive, temporally extended process and identifies key mismatches between clinician cognition and current AI design. These results provide concrete directions for the development of AI systems that more effectively align with and augment real-world clinical reasoning.

翻译：随着医生借助AI系统来满足诊疗速度与护理质量的双重需求，他们面临着模型幻觉与奉承迎合的挑战。理解医生在真实临床环境中如何进行推理，对于设计有效的AI推理系统至关重要。尽管近期医学AI领域的进展聚焦于性能基准与诊断准确率，但对临床医生推理过程随时间展开的结构性特征——例如他们如何与电子健康记录交互、在不确定性与约束条件下如何运作——却鲜有关注。本研究采用混合研究方法，结合质性访谈与结构化调查数据，对临床推理及其与当前AI辅助工作流程的关系进行了基于实证的全面阐释。研究结果表明，现有AI系统主要用于文档记录、摘要生成等就诊层面的任务，仅部分契合医生的底层推理过程。具体而言，AI生成的表征常缺失临床决策中关键的时间结构或解释性结构，而推理的核心维度（尤其是跨多次就诊的推理）仍主要由医生主导且呈隐式状态。通过将精细化的质性洞见与广泛的量化模式相结合，本研究构建了一个统一框架，将临床推理理解为一种情境敏感、时间延展的过程，并识别出临床医生认知与当前AI设计之间的关键错位。这些发现为开发更有效对齐并增强真实临床推理的AI系统提供了具体方向。