Clinical Reasoning in the Age of AI: Longitudinal Cognition and Human-AI Collaboration

2026-06-06

AI Summary

Current AI systems predominantly focus on single-encounter documentation tasks and struggle to support clinicians’ dynamic, context-sensitive reasoning across multiple visits, often deviating from authentic clinical cognition due to hallucinations and overly accommodating outputs. This study employs a mixed-methods approach—integrating in-depth interviews and structured questionnaires—to systematically analyze how physicians navigate uncertainty, interact with electronic health records, and perform longitudinal clinical reasoning in real-world settings. The findings uncover critical mismatches between existing AI tools and clinicians’ cognitive workflows and, for the first time, propose a unified framework that synthesizes qualitative insights with quantitative patterns. This framework offers a theoretical foundation and design guidance for developing next-generation AI systems that are temporally aware, interpretable, and seamlessly aligned with clinical practice.

Abstract

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.

Problem

• clinical reasoning
• human-AI collaboration
• temporal cognition
• AI alignment
• medical decision-making

Innovation

• clinical reasoning
• human-AI collaboration
• longitudinal cognition
• temporal structure
• AI-mediated workflows