Healthcare

Follow this topic Ask about this topic

Healthcare AI uses patient records, medical images, signals, and conversations to support tasks such as diagnosis, risk prediction, treatment planning, and care coordination. Common approaches include supervised learning, multimodal foundation models, retrieval-augmented generation, knowledge graphs, and reinforcement learning. Systems are tested on resources such as MIMIC-IV, MedQA, and CheXpert, as well as through clinician studies and workflow evaluations. A central challenge is that strong benchmark results do not guarantee safe, fair, reliable performance with incomplete information, changing clinical settings, or real patients, so prospective validation and careful governance remain essential.

Latest papers

622 papers24 papers in the last four weeks, down 40% on the four weeks before. 0.1% of all new papers.

Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context
Sep 24, 2025Andrew Wang, Jiashuo Zhang, Michael OberstChest X-Ray Classification Distribution Shift
Brown University · Johns Hopkins University
Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies
Sep 13, 2025Yuping Wu, Viktor Schlegel, Warren Del-Pinto +11Medical Report Generation Healthcare
Univeristy of Manchester · Imperial College London, Imperial Global Singapore · Khoo Teck Puat Hospital, Singapore +2
General Demographic Pre-trained Models for Enhancing Predictive Performance Across Diseases and Population
Sep 9, 2025Li-Chin Chen, Ji-Tian Sheu, Yuh-Jue ChuangHealthcare Representation Learning
Data Analytics and Digital Transformation Research Center National Taiwan University Taipei, Taiwan · Department of Health Care Management Chang Gung University Taoyuan, Taiwan
MedRepBench: A Comprehensive Benchmark for Medical Report Interpretation
Aug 21, 2025Fangxin Shang, Yuan Xia, Dalu Yang +2VLM Evaluation Healthcare
Baidu Inc., China
ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs
Aug 19, 2025Hongxin Ding, Baixiang Huang, Yue Fang +8RL for Language Models Healthcare
National Engineering Research Center of Software Engineering, Peking University, China · School of Computing and Data Science, The University of Hong Kong · School of Computer Science, Peking University, Beijing, China +2
Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming
Jul 30, 2025Jiazhen Pan, Bailiang Jian, Paul Hager +20LLM Safety Benchmarks Language Model Safety Evaluation
Cherise · School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany · Department of Engineering Science, University of Oxford, Oxford, United Kingdom +9
HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs
Jul 29, 2025Kaixuan Wang, Chenxin Diao, Jason T. Jacques +2Healthcare Question Answering
University of Oxford, United Kingdom · University of Edinburgh, United Kingdom · University of St Andrews, United Kingdom +1
PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data
Jul 26, 2025Aishwarya Mandyam, Jason Meng, Ge Gao +4Off-Policy Evaluation Healthcare
Stanford University · Gladstone Institutes · Stanford University[cs.LG]
Instance Generation for Patient-to-room Assignment and Admission Scheduling Based on Real Hospital Data
Jul 4, 2025Tabea Brandt, Christina Büsing, Johanna Leweke +2Healthcare Dynamic Programming
Chair of Combinatorial Optimization, RWTH Aachen University, Im Süsterfeld 9, 52072 Aachen, Germany · Chair of Algorithms and Complexity, RWTH Aachen University, Ahornstraße 55, 52074 Aachen, Germany
Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization
Jun 8, 2025Pengfei Hu, Xiaoxue Han, Fei Wang +1Healthcare Domain Generalization
School of Computing Stevens Institute of Technology Hoboken, New Jersey, United States · Weill Cornell Medicine Cornell University New York, New York, United States
Generalizable dose denoising using diffusion transformer framework for pencil beam scanning proton therapy
Jun 4, 2025Yuzhen Ding, Hongying Feng, Jianfeng Zhao +14Diffusion-Based Denoising Healthcare
Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA · College of Mechanical and Power Engineering, China Three Gorges University, Yichang, Hubei 443002, China · Department of Radiation Oncology, Guangzhou Concord Cancer Center, Guangzhou, Guangdong, 510555, China +1
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
May 28, 2025Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati +7Federated Learning Aggregation Differentially Private Stochastic Gradient Descent
German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany · Department of Computer Engineering, Bilkent University, Universiteler 06800 Çankaya/Ankara, Türkiye · Medical Research Group, MLCommons, San Francisco, CA, USA +2
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
May 27, 2025Alexander Jaus, Zdravko Marinov, Constantin Seibold +4Image Segmentation Medical Imaging Foundation Models
Karlsruhe Institute of Technology, Germany · HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany · Diagnostic and Interventional Radiology, Heidelberg University Hospital +1
Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study
May 17, 2025Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein +3Online Resource Allocation Healthcare
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA. · Department of Pediatrics (Cardiology), Stanford University, CA 94305, US. · Department of Bioengineering, Stanford University, CA 94305, US.
Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools
Apr 2, 2025Lena Schmidt, Oshin Sharma, Chris Marshall +1Healthcare LLM Reranking
NIHR Innovation Observatory, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
UniReg: Conditional Unified Model for Medical Image Registration
Mar 17, 2025Zi Li, Jianpeng Zhang, Tai Ma +7Healthcare Medical Image Registration
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China · DAMO Academy, Alibaba Group · The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China +1
Histopathology Multi-modal Embedding for Pathology Composed Retrieval
Feb 11, 2025Qifeng Zhou, Wenliang Zhong, Thao M. Dang +4Computational Pathology Healthcare
The University of Texas at Arlington, Arlington TX 76010, USA
Learning Personalized Prompts for Healthcare Guidance
Dec 20, 2024Ruize Shi, Hong Huang, Wei Zhou +3Prompt Learning LLM Personalization
Huazhong University of Science and Technology, Wuhan, China · 1Huazhong University of Science and Technology, Wuhan, China · 2Tongji Medical College +1
How Well Can Modern LLMs Act as Agent Cores in Radiology Environments?
Dec 12, 2024Qiaoyu Zheng, Chaoyi Wu, Weike Zhao +5Healthcare LLM Agent Evaluation
Shanghai Jiao Tong University, Shanghai, China · Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
MultiViewDx: Evidence-Linked Multi-View Clinical Diagnosis
Oct 19, 2024Junda Wang, Zonghai Yao, Yujan Ting +5Healthcare Medical Image Analysis
United Imaging Intelligence, MA, USA · Manning College of Information and Computer Sciences, University of Massachusetts Amherst, MA, USA · Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA +1
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Mar 18, 2024Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed +4Healthcare Oncology
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA · Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA · Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Mar 7, 2024Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5Healthcare Medical Diagnosis
2Indian Institute of Technology Bombay · 1IBM Research

Sep 24, 2025J/K move · Enter open · S save

Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context

Andrew Wang, Jiashuo Zhang, Michael Oberst

Brown University · Johns Hopkins University

Chest X-Ray Classification Distribution Shift Healthcare Medical Image Benchmarks

Open paper AskPDF arXiv

Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare. However, the reported strong average-case performance of these models do not necessarily reflect their actual utility when used in heterogeneous clinical settings, potentially masking weaker performance in medically significant scenarios. In this work we use clinical context to provide a more holistic evaluation of models for CXR diagnosis. In particular, we use discharge summaries, recorded prior to each CXR, to derive a ``pre-CXR'' probability of each CXR label, as a proxy for existing contextual knowledge available to clinicians when interpreting CXRs. We use this measure to probe model performance along two dimensions: First, using a stratified analysis, we show that models tend to have lower performance (as measured by AUROC and other metrics) among individuals with higher pre-CXR probability. Second, by controlling for pre-CXR probability via matching and re-weighting, we demonstrate that performance degrades when the correlation is broken between prior context and the current CXR label, suggesting that model performance is highly sensitive to the underlying distribution of clinical context. Specifically, cases with high pre-test probabilities present a fundamentally more difficult visual classification task, highlighting a gap in clinical utility when models are applied to high-risk cohorts.