Medical LLMs

LLM: Large Language Model

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26 papers in the last four weeks, up 160% on the four weeks before. 0.3% of all new papers.

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Latest papers 238

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Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI
Oct 6, 2026Yexiao He, Yucheng Tang, Pengfei Guo +6Medical LLMs Continual Learning for VLMs
University of Maryland, College Park · NVIDIA
Large language models are vulnerable to incidental information in clinical documentation and reasoning
Oct 6, 2026Krithik Vishwanath, Brandon Ye, Anton Alyakin +4Medical Report Generation Clinical Language Model Evaluation
Department of Neurosurgery, NYU Langone Health, New York, NY, USA · Johns Hopkins University School of Medicine, Baltimore, MD, USA · Washington University School of Medicine in St. Louis, St. Louis, MO, USA +7
SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis
Oct 6, 2026Chuan Li, Chengyu Wang, Cen Chen +3Synthetic Data Generation Biomedical QA
East China Normal University, Shanghai, China · Alibaba Group, Hangzhou, China
Closing Ambient Clinical Documentation Gaps with Automated Provider Queries
Oct 5, 2026Joseph Paul Cohen, Raj Shah, Han-Chin Shing +4Medical Report Generation Automated Medical Coding
Amazon
Conditional Rank Allocation for Taxonomy-Aware Medical Language Model Adaptation
Oct 5, 2026Guangyuan Dong, Ziwei Hong, Xuehao Zhou +6Low-Rank Adaptation Domain Adaptation for LLMs
National University of Singapore, Singapore · University of Pennsylvania, USA · Department of Urology, Shanghai General Hospital, Shanghai, China +3
Aligning Multimodal Patient Evidence with Biomedical Knowledge Graphs for Clinical LLMs
Oct 5, 2026Jiawen Du, Arshan Ali Khan, Chenhao Zhang +7KG Construction Graph-Based Retrieval
University of North Carolina at Chapel Hill · Siddaganga Institute of Technology · Foci Labs +1
Ontology Concept Overlap as a Training Signal: Knowledge-Grounded Reinforcement Learning for Clinical Question Answering
Oct 5, 2026Aditya Tanna, Abhishek JindalReward Modeling RL for Language Model Reasoning
Dhirubhai Ambani University Gandhinagar, Gujarat, India
HuatuoGPT-3: RL-Only Domain Adaptation from Base Models
Oct 5, 2026Junying Chen, Xinyuan Xie, Ziniu Li +7RL for Language Models Reinforcement Learning
The Chinese University of Hong Kong, Shenzhen · Shenzhen Research Institute of Big Data · Shenzhen Loop Area Institute +1
Single-Pass Uncertainty Heads for Claim-Level Hallucination Detection in Persian Medical Language Models
Oct 2, 2026Mehrdad Ghassabi, Pedram Rostami, Hamidreza Baradaran Kashani +2Hallucination Detection LLM Hallucination Detection
Faculty of Computer Engineering University of Isfahan Isfahan, Iran · School of Electrical and Computer Engineering University of Tehran Tehran, Iran · School of Computer Science University of Windsor Windsor, Canada +1
Clinical Concept Centers in LLMs
Oct 2, 2026Aishik Nagar, Abhishek Vaidyanathan, Arun-Kumar Kaliya-Perumal +2Mechanistic Interpretability Causal Interventions in Language Models
Carnegie Mellon University (CMU) · National University of Singapore · SAP Asia Pte Ltd +2
A Multi-Agent LLM Framework for Personalized Health Checkup Interpretation and Guidance
Oct 1, 2026HyungJun Kim, Taehan Lee, Soojin CheonMulti-Agent LLM Systems Healthcare
DaNaA Data Co., Ltd.
Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning
Sep 30, 2026Taye Akinrele, Noorbakhsh Amiri Golilarz, Subash Neupane +2Long-Context Language Model Evaluation Clinical Reasoning
Department of Computer Science, The University of Alabama
Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports
Sep 30, 2026Aawez Mansuri, Kush Mehta, Mohammadreza Chavoshi +10Supervised Fine-Tuning Synthetic Data Generation
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA · Department of Computer Science, Emory University, Atlanta, GA, USA
OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction
Sep 30, 2026Mingchen Li, Rohan Pandey, Junhui Qian +3Mixture of Experts Longitudinal EHR Modeling
Manning College of Information and Computer Sciences, UMass Amherst, Amherst, MA, USA · Center for Healthcare Organization and Implementation Research, VA Bedford Health Care, Bedford, MA, USA · Miner School of Computer and Information Sciences, UMass Lowell, Lowell, MA, USA
PrivMeSA: Privacy-Aware Self-Evolving Multi-Agent System for Medicine via Local-Remote LLM Collaboration
Sep 29, 2026Dannong Wang, Yuran Zhang, Bian Sun +4Multi-Agent LLM Systems Privacy-Preserving Language Models
Institute of Artificial Intelligence, University of Central Florida · Department of Computer Science and Operations Research, Universit´e de Montr´eal · Department of Medicine, University of Central Florida
Applying Language Models in Clinical Medicine: Recent Trends and Perspectives
Sep 28, 2026Erik AertsLLM Reliability LLM Safety
Recursive LLM Degradation in Biomedical Question Answering: A Cross-Generation Study
Sep 28, 2026Bibek Bhandari, Kshitij LingthepBiomedical QA Synthetic Data Generation for Language Models
Independent Researcher Memphis, Tennessee, USA
PCQC: Privileged Counterfactual Question Credit for Multi-Turn Medical Dialogue
Sep 23, 2026Chenxuan Li, Jiayi Wan, Xinrong Chen +3Clinical Reasoning Medical LLMs
Peking University, Beijing, China · Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University, Beijing, China
Syndrome, Synergy, and Safety: Structured Reasoning and Knowledge-Driven Alignment for TCM Prescription Generation
Sep 22, 2026Zheng Chen, ZhiCheng Du, Haoxuan Li +1LLM Alignment CoT Reasoning
Tsinghua University · Guangdong Provincial Laboratory of Traditional Chinese Medicine Hengqin
LingLan: An Advancing Traditional Chinese Medicine Diagnosis LLM with Multimodal Data
Sep 22, 2026Zheng Chen, Zhicheng Du, Haoxuan Li +2Medical Diagnosis Multimodal Large Language Models
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Guangdong Provincial Laboratory of Traditional Chinese Medicine Hengqin, Guangdong, China
From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health
Sep 21, 2026He Hu, Yucheng Zhou, Qianning Wang +8Personalized Language Models Medical LLMs
When Quantization Preserves Accuracy but Not Evidence: Explanation-Aware Post-Training Quantization for Medical LLMs
Sep 21, 2026Yeji Kim, Mi-Young Kim, Randy GoebelLLM Quantization Faithfulness of Language Model Explanations
University of Alberta
Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards
Sep 21, 2026Kalash Shah, Kunal Singh, Snehan J +1RL for Language Model Reasoning Rubric-Based RL
Fractal AI Research
Rewarding Reasoning, Not Answers: Fixing and Bounding Test-Time Reinforcement Learning on Medical QA
Sep 15, 2026Kailong Fan, Anqi Pu, Yichen Wu +7Process Reward Models RL for Language Model Reasoning
1Harvard Medical School/MGH · 3Harvard University · 2Zhejiang University
CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine
Sep 14, 2026Shuai Wang, Yize Zhao, Qingyu ChenRetrieval-Augmented Generation Knowledge Conflicts in Language Models
Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA · Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA
EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse
Sep 14, 2026Dongsheng Shi, Yue Li, Xin Yi +1Continual Learning for LLM Agents Multi-Agent LLM Systems
East China Normal University · City University of Hong Kong
Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes
Sep 14, 2026Pallika Kafle, Yipeng Zhou, Guanfeng Liu +2Healthcare Clinical NLP
Macquarie University, Sydney, Australia · Beijing Normal–Hong Kong Baptist University, Zhuhai, China · National Tsing Hua University, Hsinchu, Taiwan
Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection
Sep 13, 2026Zeyu Dong, Benjamin Wang, Joyee W. JinAdaptive Inference Language Model Decoding
St. Theresa of Lisieux Catholic High School Toronto, Canada · Department of Computer Science University of Toronto Toronto, Canada
A primer on evaluation methods for large language models in healthcare
Sep 13, 2026Suzannah E McKinney, Phuc Vu, Samuel A Justice +7LLM Evaluation Clinical Language Model Evaluation
Mass General Brigham AI, Mass General Brigham, Boston, MA, USA · Generative AI Laboratory, School of Informatics, University of Edinburgh, Edinburgh, UK · University of Edinburgh Centre for Cardiovascular Science, Edinburgh, UK +5
Scaling Clinical Judgment to Evaluate Medical AI
Sep 11, 2026Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur +15LLM-as-a-Judge LLM Fine-Tuning
Department of Biomedical Informatics, Harvard Medical School, Boston, MA · Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA · Stanford Division of Hospital Medicine, Stanford University, Stanford, CA +11

Oct 6, 2026, cs.AIJ/K move · Enter open · S save

Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI

Yexiao He, Yucheng Tang, Pengfei Guo, Yufan He+5

University of Maryland, College Park · NVIDIA

Medical LLMs Continual Learning for VLMs Online Continual Learning Memory-Augmented VLMs Medical VLMs

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Large language models (LLMs) and vision-language models (VLMs) are usually frozen after deployment, so they do not learn from the cases they solve. This is especially concerning in medicine, where new clinical evidence, updated guidelines, and new therapies can change established practice. Fine-tuning can update the model, but it requires access to model weights and additional training. Parameter-free methods avoid training, but they may overfit a fixed validation set, lack reliable domain knowledge, or lose visual details by saving experience only as text. To address these limitations, we present a model-agnostic framework that allows frozen LLMs and VLMs to learn from deployment experience through three forms of external expertise: a Skill that guides reasoning and tool use, a Knowledge Memory that stores reliable facts supported by earlier cases or trusted external evidence, and a Multimodal Knowledge Base that keeps visual examples and guides the model to relate each retrieved case to the current image. Instead of relying on a fixed validation set, a validation strategy keeps an update only if it helps on new cases without degrading performance on earlier ones. Across six benchmarks covering clinical diagnosis, clinical workflows, medical reasoning, and medical and non-medical visual reasoning, and with four open-weight and closed-source base models, our framework improves performance during online deployment by up to 34.2% over the base model on medical tasks, generalizes to unseen cases, transfers to other models without further optimization, and works in non-medical domains.

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Latest papers 238