Clinical Reasoning and Diagnostic Skills

Últimos artículos

• A rubric landscape for evaluating clinical reasoning in large language models: what exists, what is missing, and what needs to be combined

  Zhangshu Joshua Jiang, Zina Ibrahim, James T. Teo · 2 de octubre de 2026

  Exam-style accuracy does not establish whether large language models (LLMs) reason well over clinical records. We define clinical reasoning as integrating and updating evidence across time and sources to form, revise and justify a patient's problem representation and a defensible plan. This struct…

• Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning

  Taye Akinrele, Noorbakhsh Amiri Golilarz, Subash Neupane, Sudip Mittal, Shahram Rahimi · 2 de octubre de 2026

  Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient histories. Although long-context models can process increasingly large amounts of information, providing more history does not necessarily make releva…

• From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning

  Minye Shao, Chaohui Yu, Yixuan Wu, Fan Wang, Ling Shao, Yang Long · 1 de octubre de 2026

  Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected evidence rather than the ability to seek it across clinical records and longitudinal imaging. We propose CASE: a series of role-specific Clinical Agen…

• KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy

  Xueting Fang, Zehui Li, Yang Yang, Camilla Giovino, Shubh K. Patel, Shailly Prajapati, Vallijah Subasri, Caihua Shan · 1 de octubre de 2026

  Most clinical benchmarks evaluate language models (LMs) on diagnosis using complete case descriptions. In clinical practice, however, patients present information in different ways, and clinicians must obtain relevant history and determine which examinations are needed before reaching a diagnosis. D…

• Can LLMs Value the Right Evidence? Evidence-Value Misalignment in Dynamic Medical Diagnosis

  Kehua Feng, Yunsheng Lu, Yitong Qiao, Tiantian He, Lei Liu, Yue Shen, Jian Wang, Jinjie Gu · 30 de septiembre de 2026

  A correct diagnosis reached from insufficient or misleading evidence can pose a clinical hazard, yet outcome-based accuracy may reward such lucky guesses. We call this mismatch between diagnostic decisions and the value of available evidence Evidence-Value Misalignment (EVM). To disentangle evidenti…

• ConsultMind:Towards Automated Diagnostic Consultation via Uncertainty-Aware Reasoning

  Xiao Sun, Yuming Yang, Yun Chen, Jiang Zhong, Junnan Zhu, Xinyi Jiang, Haoyang Zeng, Ruirui Chen, Yining Wang, Xinyu Zhou, Rong Tang, Kaiwen Wei · 28 de septiembre de 2026

  Diagnostic consultation is an online sequential decision-making process in which clinicians gather evidence through patient interaction until a diagnosis is sufficiently supported. Automating this process requires adaptive inquiry and interpretable decisions. Bayesian networks offer a natural founda…

• An auditable conditional-strategy framework for open-ended decision-making in complex lung cancer

  Daoyun Wang, Zhicheng Huang, Huaiyuan Sun, Jiaqi Xu, Xiaowei Xu, Zhibo Zheng, Zhongxing Bing, Yuxiao Lin, Yicheng Liang, Chao Gao, Bowen Xue, Kai Zhang, Song Xu, Wanpu Yan, Hui Xia, Lin Li, Xiang Yan, Mu Hu, Qianli Ma, Zhiqiang Xue, Xiaofang Liu, Zhihai Han, Nan Zhang, Chuanhao Tang, Tongmei Zhang, Lan Song, Zhaohui Zhu, Xuan Zeng, Shafei Wu, Hui Guan, Lei Deng, Huaxia Yang, Zeliang Lian, Wubin Sun, Yongxin Wang, Xiaohui Shen, Binlin Wang, Tiantian Gu, Yu Cui, Li Zhang, Shirui Wang, Naixin Liang · 25 de septiembre de 2026

  Complex lung cancer decisions can involve several defensible pathways whose eligibility, sequencing and safety depend on unresolved information. Effective support must make explicit how patient conditions govern pathway eligibility, deferral and redirection. MedGPT Clinical Explorer (MCE) organizes …

• PCQC: Privileged Counterfactual Question Credit for Multi-Turn Medical Dialogue

  Chenxuan Li, Jiayi Wan, Xinrong Chen, Zhongyu Zhao, Xuecheng Shang, Peixing Wan · 24 de septiembre de 2026

  Large language models (LLMs) have made substantial progress on medical question-answering, yet effective medical dialogue also requires learning to ask questions that uncover relevant patient information. To train such dialogue policies, a common pipeline combines supervised fine-tuning with reinfor…

• Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards

  Kalash Shah, Kunal Singh, Snehan J, Shreyas Singh · 22 de septiembre de 2026

  Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the convergent, evidence-driven task of inferring a patient's condition from clinical data to produce a diagnosis, and clinical healthcare reasoning: the broad…

• Rewarding Reasoning, Not Answers: Fixing and Bounding Test-Time Reinforcement Learning on Medical QA

  Kailong Fan, Anqi Pu, Yichen Wu, Wanhua Li, Yicong Li, Hanspeter Pfister, Huafeng Liu, Xiang Li, Quanzheng Li, Ning Guo · 16 de septiembre de 2026

  Test-time reinforcement learning adapts a model on its own unlabeled test set using majority-vote pseudo-labels and has shown strong results in mathematics. We show that this recipe collapses on medical multiple-choice QA: accuracy stagnates while output diversity rapidly declines. Through a control…

• Uncertainty-Aware Calibrated Clinical Text Classification with Large Language Models

  Mridul Sharma, Adeetya Patel, Zaneta D' Souza, Samira Abbasgholizadeh Rahimi, Siva Reddy, Sreenath Madathil · 16 de septiembre de 2026

  Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect patient care. Existing black-box uncertainty methods attach a confidence score to a fixed LLM prediction using softmax probabilities, verbalised confidence, prompt…

• EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse

  Dongsheng Shi, Yue Li, Xin Yi, Linlin Wang · 15 de septiembre de 2026

  Large language model (LLM) driven multi-agent systems have shown promise in complex clinical reasoning, yet existing approaches rely on static strategies and lack persistent clinical memory, preventing self-evolving from prior diagnostic successes and failures. We present EMR, a self-evolving medica…

• VeriDx: Earning the Right to Diagnose with Disease-Centric Verification

  Zhong Cao, Shuying Chen · 15 de septiembre de 2026

  A correct diagnosis can still be reached for the wrong reasons. In clinical reasoning, every disease hypothesis creates obligations: key evidence must be checked, alternatives must be ruled out, contradictions must be resolved, useful tests must be considered, and closure must be justified. Current …

• Asclepius: An Adaptive Harness for Long-Horizon Clinical Agents

  Grace Chang Yuan, Xiaoman Zhang, Sung Eun Kim, Luyang Luo, Pranav Rajpurkar · 15 de septiembre de 2026

  LLM agents are predominantly benchmarked on short, single-task trajectories, yet real deployments run for hours under contention, surfacing a different class of failures. We use the Clinical Environment Simulator (CES), in which an agent manages an entire emergency-department shift under continuous …

• MedRoundsQA: A Persona and Difficulty Aware Evaluation for Multi-Turn Medical Consultations

  Youssef Mohamed, Ahmed Heakl, Qinrong Cui, Junhong Liang, Rafiq Ali, Bdour Babillie, Nazira Dunbayeva, Lang Gao, Omar Hussein, Ahmed Nada, Ahmed Mohamed Magdy Mohamed, Jinghui Liu, Salman Khan, Imran Razzak, Yuxia Wang, Xiuying Chen · 14 de septiembre de 2026

  Medical benchmarks are dominated by single-turn, multiple-choice clinical cases that poorly reflect real consultations. Practically, clinicians elicit evidence interactively and patient communication varies widely. We introduce MedRoundsQA, a multi-turn diagnostic benchmark derived from 1,387 board-…

• Scaling Clinical Judgment to Evaluate Medical AI

  Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur, Byron Crowe, Anthony M. Pettinato, Aashna P. Shah, Adrian D. Haimovich, Liam G. McCoy, Daniel Restrepo, Ethan Goh, Jonathan H. Chen, Laura Zwaan, Katherine E. Goodman, Daniel J. Morgan, Raja-Elie E. Abdulnour, Adam Rodman, Arjun K. Manrai · 14 de septiembre de 2026

  Blinded physician evaluation has been considered by many to be the gold standard for assessing clinical reasoning in large language models (LLMs). This is difficult to scale; thus, prior studies typically rely on small physician panels, often from a single institution or specialty, which both limits…

• Performance of Clinical AI System and Physicians and Frontier Language Models in primary care diagnostics

  Andy Nkansah, Hanna Plotnitskaya, Stanislau Salavei, Anna Kozlova, Piotr Gibas, Julian Milek, Viktar Harbachou, Aleksey Ropan, Pavel Satalkin · 9 de septiembre de 2026

  Clinical AI evaluation should encompass diagnosis and management after adaptive information gathering. We compared Doctorina, eight physicians and four standalone frontier language models in 150 synthetic Polish-language primary-care consultations. Doctorina achieved 82.0% Top-1 concordance versus 5…

• A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision Support

  Chang Xia, Leilei Ouyang, Huimin Wang, Yong Zhao, Kang Li · 7 de septiembre de 2026

  Large language models (LLMs) show potential for medical tasks, but their single-turn question-answer format does not reflect how clinical diagnosis is performed in practice. As a result, they remain limited in complex diagnostic settings. We developed Debate-Mixture-of-Agents (DMoA), a novel multi-a…

• Constructing and Evaluating Clinical Reasoning Trajectories for Medical Agent

  Yunqi Zhu, Wensheng Zhang, Xuebing Yang · 7 de septiembre de 2026

  Evaluation of medical artificial intelligence agents remains predominantly answer-centric, assessing only the correctness of final outputs while overlooking the quality of intermediate reasoning. In clinical settings, however, a correct answer reached through fabricated evidence or incoherent logic …

• Untangling the Mechanisms of Misleading Context in Medical Question Answering

  Robin Linzmayer, No\'emie Elhadad · 3 de septiembre de 2026

  Large language models now answer medical questions with expert-level performance. However, the context these systems act on can be misleading, and misleading context can corrupt a model's medical judgment. To understand how misleading context corrupts this judgment, we examine the model's susceptibi…

• Examining the Vulnerability of Multi-Agent Medical Systems to Human Interventions for Clinical Reasoning

  Benjamin C Liu, Dillon Mehta, Rishi Malhotra, Adam Zobian, Yong Ying Tan, Samir Chopra, Daniella Rand, Natalie Pang, Abhiram Gudimella, Kevin Zhu · 3 de septiembre de 2026

  Human interventions at fault points can alter the diagnostic accuracy of multi-agent medical systems. We defined fault points as moments in AI agent conversations, in which an agent's reasoning became most vulnerable to external influence. Using the MedQA dataset, this study analyzed simulated docto…

• SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LLMs' Robustness to Contradictory Evidence

  Yi Yu, Bo Wang, Chong Feng, Ge Shi, Xia Liu, Ziyi Yang, Xuewen Shi · 1 de septiembre de 2026

  Current evaluations of large language models (LLMs) primarily focus on factual knowledge retrieval, overlooking the fundamental challenge of navigating the complex, non-bijective mappings between clinical indicators and diagnoses. Existing benchmarks fail to assess whether large language models trul…

• From GenAI Virtual Patient Dialogue Logs to Teacher-Interpretable Process Evidence: A Learning Analytics Study in Higher Education

  Xinyu Li, Zijian Li, Mengyu Xia, Luzhen Tang, Naping Chen, Changmin Lin, Danijela Gasevic, Dragan Gasevic, Yizhou Fan · 1 de septiembre de 2026

  Medical history taking is a dialogue-based clinical reasoning task in which learners must gather, organise, and integrate patient information while the consultation unfolds. Generative AI-powered virtual patients (GenAI VPs) make repeated history taking practice scalable and preserve full turn by tu…

• From Uncertainty to Clinical Risk: Severity-Aware Conformal Planning for Interactive Medical Diagnosis

  Yue Zhou, Haiyang Zhou, Jin Zhang, Kong Wang, Yongxin Ni, Youhua Li, Hanwen Du · 31 de agosto de 2026

  Interactive medical diagnosis dynamically acquires patient information through multiple rounds of questioning, supporting accurate, efficient, and safe clinical decisions under incomplete evidence. Existing methods commonly guide information acquisition with predictive uncertainty or label ambiguity…

• CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models

  Yucheng Zhou, Peng Luo, Qianning Wang, Chengzhong Xu, Jianbing Shen · 28 de agosto de 2026

  Large Language Models (LLMs) have shown strong potential for medical reasoning, yet the scarcity and cost of expert-annotated data constrain their progress. While reinforcement learning offers a scalable alternative, standard outcome-based methods in medicine often suffer from autoregressive credit …