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Latest papers 533

38 papers in the last four weeks, up 41% on the four weeks before. 0.3% of all new papers.

Apr 16, 2026cs.CL

Continued Pretraining of FinBERT on Finnish Histopathological Reports: Train-Time Signals and Proxy Downstream Correlations

In Natural Language Processing (NLP) classification tasks where a lack of labeled data is an issue, continued pretraining (CPT) of transformer models on unlabeled data is an established approach. In this paper, we have two aims. (1) We describe our observations from continued pretraining of the Finnish BERT transformer model (FinBERT) on a Finnish histopathological dataset (below, \emph{the Histopathology data}). (2) Since the Histopathology data has no classification labels, we gather public Finnish datasets as proxy data to analyze whether the signals observed in (1) are associated with downstream classification gains. We observe that CPT train-time loss curves differ strongly by domain, and that, in an exploratory analysis, certain CPT-derived features correlate with proxy classification improvement. In particular, this report contributes to the limited literature on NLP for Finnish healthcare data.

Rami Luisto, Liisa Petäinen, Tommi Grönholm +5

Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland. · Digital Workforce Services, Helsinki, Finland. · Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland. +2

Healthcare Domain-Adaptive Pretraining Continued Pretraining Clinical Language Model Evaluation Clinical NLP

Apr 16, 2026cs.AI

Rethinking Patient Education as Multi-turn Multi-modal Interaction

Most medical multimodal benchmarks focus on static tasks such as image question answering, report generation, and plain-language rewriting. Patient education is more demanding: systems must identify relevant evidence across images, show patients where to look, explain findings in accessible language, and handle confusion or distress. Yet most patient education work remains text-only, even though combined image-and-text explanations may better support understanding. We introduce MedImageEdu, a benchmark for multi-turn, evidence-grounded radiology patient education. Each case provides a radiology report with report text and case images. A DoctorAgent interacts with a PatientAgent, conditioned on a hidden profile that captures factors such as education level, health literacy, and personality. When a patient question would benefit from visual support, the DoctorAgent can issue drawing instructions grounded in the report, case images, and the current question to a benchmark-provided drawing tool. The tool returns image(s), after which the DoctorAgent produces a final multimodal response consisting of the image(s) and a grounded plain-language explanation. MedImageEdu contains 150 cases from three sources and evaluates both the consultation process and the final multimodal response along five dimensions: Consultation, Safety and Scope, Language Quality, Drawing Quality, and Image-Text Response Quality. Across representative open- and closed-source vision-language model agents, we find three consistent gaps: fluent language often outpaces faithful visual grounding, safety is the weakest dimension across disease categories, and emotionally tense interactions are harder than low education or low health literacy. MedImageEdu provides a controlled testbed for assessing whether multimodal agents can teach from evidence rather than merely answer from text.

Zonghai Yao, Zhipeng Tang, Chengtao Lin +5

1VA Bedford Health Care · 2UMass Amherst · 4Yale University +3

Multimodal Grounding Healthcare Multimodal Agents Multimodal Model Evaluation Medical Image Benchmarks

Apr 16, 2026cs.CL

Retrieve, Then Classify: Corpus-Grounded Automation of Clinical Value Set Authoring

Clinical value set authoring -- the task of identifying all codes in a standardized vocabulary that define a clinical concept -- is a recurring bottleneck in clinical quality measurement and phenotyping. A natural approach is to prompt a large language model (LLM) to generate the required codes directly, but structured clinical vocabularies are large, version-controlled, and not reliably memorized during pretraining. We propose Retrieval-Augmented Set Completion (RASC): retrieve the

K

most similar existing value sets from a curated corpus to form a candidate pool, then apply a classifier to each candidate code. Theoretically, retrieve-and-select can reduce statistical complexity by shrinking the effective output space from the full vocabulary to a much smaller retrieved candidate pool. We demonstrate the utility of RASC on 11,803 publicly available VSAC value sets, constructing the first large-scale benchmark for this task. A cross-encoder fine-tuned on SAPBert achieves AUROC~~0.852 and value-set-level F1~~0.298, outperforming a simpler three-layer Multilayer Perceptron (AUROC~~0.799, F1~~0.250) and both reduce the number of irrelevant candidates per true positive from 12.3 (retrieval-only) to approximately 3.2 and 4.4 respectively. Zero-shot GPT-4o achieves value-set-level F1~0.105, with 48.6% of returned codes absent from VSAC entirely. This performance gap widens with increasing value set size, consistent with RASC's theoretical advantage. We observe similar performance gains across two other classifier model types, namely a cross-encoder initialized from pre-trained SAPBert and a LightGBM model, demonstrating that RASC's benefits extend beyond a single model class. The code to download and create the benchmark dataset, as well as the model training code is available at: https://github.com/mukhes3/RASC.

Sumit Mukherjee, Juan Shu, Nairwita Mazumder +4

Oracle Health Data Intelligence

Healthcare Retrieval-Augmented Classification Classification Clinical NLP

Apr 13, 2026cs.MA

VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing

Scientific research based on multimodal clinical data (including medical imaging) requires coordinating clinical, radiological, programming, and biostatistical expertise, a fragmented process that bottlenecks discovery. We present VERITAS (Verifiable Epistemic Reasoning for Image-Derived Hypothesis Testing via Agentic Systems), a clinical co-scientist: a multi-agent system that autonomously tests natural-language hypotheses and produces a fully auditable evidence trail, tracing every conclusion through executable outputs from analysis plan to segmentation masks to statistical code to final verdict. Unlike prior AI-scientist systems, which mainly operate on tabular or text data, VERITAS grounds autonomous discovery directly in medical images. It decomposes the workflow into four phases handled by role-specialized agents, and introduces an epistemic evidence label framework that mechanically classifies outcomes as Supported, Refuted, Underpowered, or Invalid by jointly evaluating significance, effect direction, and study power. This distinction is critical in medical imaging, where non-significant results often reflect insufficient sample size rather than absent effects. We construct a tiered benchmark of 64 hypotheses spanning six complexity levels across cardiac and brain glioma MRI datasets. VERITAS reaches 81.4% verdict accuracy with frontier models and 71.2% with locally-hosted open-weight models (8-30B), outperforming all single-model baselines in both classes. It also produces the highest rate of independently verifiable statistical outputs (86.6%), so even its failures remain diagnosable through artifact inspection. Structured multi-agent decomposition thus substitutes for model scale while preserving the verifiability that scientific discovery demands. We release code, hypothesis bank, and evaluation pipeline at https://github.com/LucZot/veritas.

Lucas Stoffl, Benedikt Wiestler, Johannes C. Paetzold

Department of Radiology, Weill Cornell Medicine, New York, USA · Cornell Tech, New York, USA · TUM University Hospital, Munich, Germany +2

AI Agents for Scientific Discovery Healthcare Multi-Agent Systems Medical Image Benchmarks Scientific Claim Verification

Apr 13, 2026cs.CV

Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions

Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits that improve medical outcomes. In-person interventions are costly and difficult to scale, especially in resource-limited regions. Digital health interventions offer a cost-effective approach, potentially supporting independent living and self-management. Automating such interventions, especially through machine learning, has recently gained considerable attention. Ambivalence and hesitancy (A/H) play a primary role for individuals to delay, avoid, or abandon health interventions. A/H are subtle and conflicting emotions that place a person in a state between positive and negative evaluations of a behaviour, or between acceptance and refusal to engage in it. They manifest as affective inconsistency across modalities or within a modality, such as language, facial, vocal expressions, and body language. While experts can be trained to recognize A/H, integrating them into digital health interventions is costly and less effective. Automatic A/H recognition is therefore critical for the personalization and cost-effectiveness of digital health interventions. Here, we explore the application of deep learning models for A/H recognition in videos, a multi-modal task by nature. In particular, this paper covers three learning setups: supervised learning, unsupervised domain adaptation for personalization, and zero-shot inference via large language models (LLMs). Our experiments are conducted on the unique and recently published BAH video dataset for A/H recognition. Our results show limited performance, suggesting that more adapted multi-modal models are required for accurate A/H recognition. Better methods for modeling spatio-temporal and multimodal fusion are necessary to leverage conflicts within/across modalities.

Manuela González-González, Soufiane Belharbi, Muhammad Osama Zeeshan +8

Dept. of Health, Kinesiology, & Applied Physiology, Concordia University, Montreal, Canada · LIVIA, Dept. of Systems Engineering, ETS Montreal, Canada · LIVIA, Dept. of Software and IT Engineering, ETS Montreal, Canada +1

Healthcare Multimodal Emotion Recognition Ambivalence and Hesitancy Recognition

Apr 10, 2026cs.CL

SatIR: Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching

Many real-world retrieval and matching problems require more than topical relevance: a candidate must satisfy the specific constraints of one profile among many, not just be relevant to it. Clinical trials are a high-stakes instance of this challenge: they are central to evidence-based medicine, yet many struggle to meet enrollment targets, despite the availability of over half a million trials listed on ClinicalTrials.gov, which attracts approximately two million users monthly. Existing retrieval techniques, largely based on keyword and embedding-similarity matching, treat eligibility constraints as soft signals rather than binding requirements, resulting in low recall, low precision, and limited interpretability. We propose SatIR, a scalable, efficient, high-precision, high-recall, interpretable clinical trial retrieval method based on formal constraint satisfaction. Leveraging established medical ontologies, we use Large Language Models (LLMs) to convert informal reasoning -- regarding ambiguity, implicit clinical assumptions, and incomplete patient records -- into explicit, precise, controllable, and interpretable formal Satisfiability Modulo Theories (SMT) constraints. For scalable and efficient retrieval, we project the SMT matching problem onto relational algebra, enabling an efficient database implementation that retains high recall while sacrificing little precision. SatIR consistently improves eligibility-aware retrieval over similarity-based baselines on the SIGIR 2016 dataset and a benchmark derived from TREC 2022. Relative to TrialGPT-style retrieval, SatIR retrieves 32%-72% more relevant-and-eligible trials per patient on SIGIR 2016 and achieves 1.8-3.2x higher eligible-trial recall on the TREC benchmark. Retrieval is fast, requiring only 146 milliseconds per patient over 3,621 SIGIR trials.

Zikai Zhou, Yufei Jin, Yilin Xu +3

Department of Computer Science, Stanford University, Stanford, CA, USA · Samueli Electrical and Computer Engineering, UCLA, Los Angeles, CA, USA · Department of Computer Science, Emory University, Atlanta, GA, USA +1

Healthcare Satisfiability Modulo Theories Information Retrieval Biomedical IR Constraint Satisfaction

Apr 9, 2026cs.AI

IatroBench: A Pre-Registered Benchmark of Clinical Omission in Language Models

A strongly safety-trained model will provide a doctor with a benzodiazepine taper schedule, but not a patient who asks for one. The model knows the information, but how much it shares depends on the framing. We introduce IatroBench, a benchmark that evaluates models on two axes of harm (commission and omission) across 60 pre-registered clinical scenarios and 6 models. We use Claude Opus 4.6 to score model responses against a rubric written by a physician, and find that its omission scores are as well-aligned to the physician's scores as another physician's scores are. We find that when the same case is presented as a patient query and a doctor consultation (the variants also differ in register, request and the supervision a treating physician implies), all five models we test share more information with the doctor than the patient. We term this phenomenon "framing-contingent withholding." We find a mean decoupling gap of +0.38 across models (p = 0.003), and of +0.22 under an independent LLM judge (95% CI 0.10-0.36, p = 0.0014). An evaluation that focuses solely on commission harms would consider all of these cases as equally cautious refusals, but closer investigation reveals three different patterns: Claude Opus withholds information from the patient that it demonstrates knowledge of in the doctor framing. Llama 4 does poorly in both framings, so the decoupling gap cannot distinguish information withholding from incompetence. We are forced to exclude GPT-5.2 from this analysis because it returns no text for 33.2% of doctor responses, but 0% of layperson responses. A standard LLM judge rates responses as having zero omission harm in 86.6% of cases where our structured evaluations score them as omission harms. (Because our scenarios are designed to induce tension between safety and helpfulness, these statistics should be taken as only applying to this distribution.)

David Gringras

Harvard T.H. Chan School of Public Health

LLM Safety Benchmarks Healthcare LLM Safety Evaluation Clinical Language Model Evaluation

Apr 7, 2026cs.CL

MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models

Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are predominantly trained on professional literature, limiting their ability to communicate findings in the lay register required for patient-centered care. While text-centric research has actively developed resources for simplifying medical jargon, there is a critical absence of large-scale multimodal benchmarks designed to facilitate lay-accessible medical image understanding. To bridge this resource gap, we introduce MedLayBench-V, the first large-scale multimodal benchmark dedicated to expert-lay semantic alignment. Unlike naive simplification approaches that risk hallucination, our dataset is constructed via a Structured Concept-Grounded Refinement (SCGR) pipeline. This method enforces strict semantic equivalence by integrating Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) with micro-level entity constraints. MedLayBench-V provides a verified foundation for training and evaluating next-generation Med-VLMs capable of bridging the communication divide between clinical experts and patients.

Han Jang, Junhyeok Lee, Heeseong Eum +1

Seoul National University · Department of Radiology, Seoul National University Hospital · The Advanced Imaging and Computational Neuroimaging (AICON) Laboratory +2

Healthcare Medical VLMs Vision-Language Alignment Clinical NLP Medical Image Benchmarks

Apr 1, 2026cs.CL

CARE: Privacy-Compliant Agentic Reasoning with Evidence Discordance

Large language model (LLM) systems are increasingly used to support high-stakes decision-making, but they typically perform worse when the available evidence is internally inconsistent. Such a scenario exists in real-world healthcare settings, with patient-reported symptoms contradicting medical signs. To study this problem, we introduce MIMIC-DOS, a dataset for short-horizon organ dysfunction worsening prediction in the intensive care unit (ICU) setting. We derive this dataset from the widely recognized MIMIC-IV, a publicly available electronic health record dataset, and construct it exclusively from cases in which discordance between signs and symptoms exists. This setting poses a substantial challenge for existing LLM-based approaches, with single-pass LLMs and agentic pipelines often struggling to reconcile such conflicting signals. To address this problem, we propose CARE: a multi-stage privacy-compliant agentic reasoning framework in which a proprietary LLM provides guidance by generating structured categories and transitions without accessing sensitive patient data, while a local LLM uses these categories and transitions to support evidence acquisition and final decision-making. Empirically, under controlled retrospective evaluation on MIMIC-DOS, CARE achieves the best overall performance across key metrics among the evaluated LLMs and agentic workflows, showing that it can more robustly handle conflicting clinical evidence while preserving privacy.

Haochen Liu, Weien Li, Rui Song +6

University of Cambridge · McGill University · MBZUAI - Mohamed bin Zayed University of Artificial Intelligence +2

Privacy-Preserving Language Models Healthcare Clinical Decision-Making Multi-LLM Collaboration Clinical Risk Prediction

Mar 31, 2026cs.CL

Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations

Large language models (LLMs) are increasingly used for medical consultation and health information support, where safety depends not only on medical knowledge but also on robust responses to unclear, inconsistent, or misleading patient input. However, most existing medical LLM evaluations assume idealized and well-posed patient questions, limiting their realism. We study challenging patient behaviors that commonly arise in real medical consultations and complicate safe clinical reasoning. We define four clinically grounded categories of such behaviors: information contradiction, factual inaccuracy, self-diagnosis, and care resistance. For each behavior, we specify concrete failure criteria that capture unsafe responses. Building on four existing medical dialogue datasets, we introduce CPB-Bench (Challenging Patient Behaviors Benchmark), a bilingual (English and Chinese) benchmark of multi-turn dialogues annotated for these behaviors. We find that although models perform well overall, they exhibit consistent behavior-specific failures, especially when handling contradictory or medically implausible patient information. We further evaluate four intervention strategies and find inconsistent improvements, with some interventions introducing unnecessary corrections.

Yahan Li, Xinyi Jie, Wanjia Ruan +5

University of Southern California

Healthcare Clinical Reasoning LLM Safety Evaluation Clinical Language Model Evaluation Language Model Robustness Medical LLMs

Mar 23, 2026cs.AI

Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support

In this work, we propose Oph-Guid-RAG, a multimodal visual RAG system for ophthalmology clinical question answering and decision support. We treat each guideline page as an independent evidence unit and directly retrieve page images, preserving tables, flowcharts, and layout information. We further design a controllable retrieval framework with routing and filtering, which selectively introduces external evidence and reduces noise. The system integrates query decomposition, query rewriting, retrieval, reranking, and multimodal reasoning, and provides traceable outputs with guideline page references. We evaluate our method on HealthBench using a doctor-based scoring protocol. On the hard subset, our approach improves the overall score from 0.2969 to 0.3861 (+0.0892, +30.0%) compared to GPT-5.2, and achieves higher accuracy, improving from 0.5956 to 0.6576 (+0.0620, +10.4%). Compared to GPT-5.4, our method achieves a larger accuracy gain of +0.1289 (+24.4%). These results show that our method is more effective on challenging cases that require precise, evidence-based reasoning. Ablation studies further show that reranking, routing, and retrieval design are critical for stable performance, especially under difficult settings. Overall, we show how combining visionbased retrieval with controllable reasoning can improve evidence grounding and robustness in clinical AI applications,while pointing out that further work is needed to be more complete.

Shuying Chen, Sen Cui, Zhong Cao

School of Artificial Intelligence and Data Science University of International Business and Economics Beijing, China · Tsinghua University Beijing, China · Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital Heidelberg University Heidelberg, Germany

Multimodal RAG Retrieval-Augmented Generation Healthcare Medical Diagnosis Clinical Decision Support Visual Document Retrieval

Mar 20, 2026cs.CV

HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction

Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one or more anatomical sites; yet existing pathology vision-language models (VLMs) reduce this output to a flat label or free-form text. We present HiPath, a lightweight VLM framework built on frozen UNI2 and Qwen3 backbones that treats structured report prediction as its primary training objective. Three trainable modules totalling 15M parameters address complementary aspects of the problem: a Hierarchical Patch Aggregator (HiPA) for multi-image visual encoding, Hierarchical Contrastive Learning (HiCL) for cross-modal alignment via optimal transport, and Slot-based Masked Diagnosis Prediction (Slot-MDP) for structured diagnosis generation. Trained on 749K real-world Chinese pathology cases from three hospitals, HiPath achieves 68.9% strict and 74.7% clinically acceptable accuracy with a 97.3% safety rate, outperforming all baselines under the same frozen backbone. Cross-hospital evaluation confirms generalisation with only a 3.4pp drop in strict accuracy while maintaining 97.1% safety.

Ruicheng Yuan, Zhenxuan Zhang, Anbang Wang +5

College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China · Department of Bioengineering and Imperial-X, Imperial College London, London, UK · Department of Pathology, Xiangtan Maternal and Child Health Hospital, Xiangtan, Hunan, China +1

Computational Pathology Medical Report Generation Healthcare Medical VLMs Vision-Language Alignment

Mar 20, 2026cs.CL

FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment

We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assessment, using the U.S. Food and Drug Administration (FDA) drug label documents. Drug labels contain rich but heterogeneous clinical and regulatory information, making accurate question answering difficult for current language models. In collaboration with FDA regulatory assessors, we introduce FDARxBench, and construct a multi-stage pipeline for generating high-quality, expert curated, QA examples spanning factual, multi-hop, and refusal tasks, and design evaluation protocols to assess both open-book and closed-book reasoning. Experiments across proprietary and open-weight models reveal substantial gaps in factual grounding, long-context retrieval, and safe refusal behavior. While motivated by FDA generic drug assessment needs, this benchmark also provides a substantial foundation for challenging regulatory-grade evaluation of label comprehension. The benchmark is designed to support evaluation of LLM behavior on drug-label questions.

Betty Xiong, Jillian Fisher, Benjamin Newman +5

Stanford University · University of Washington · U.S. Food and Drug Administration

Document QA Medical QA Healthcare

Mar 17, 2026cs.CV

CompDiff enables fair and zero shot medical image generation across demographic intersections through compositional diffusion

Medical image generators trained on imbalanced data can fail at demographic intersections absent from training. We introduce CompDiff, which encodes age, sex and race separately and composes supervised demographic tokens alongside clinical text. Across chest radiographs and fundus images, CompDiff improves overall and subgroup fidelity relative to prompt conditioning (RoentGen-v2) and loss reweighting (FairDiffusion). It generalises in zero-shot generation to 16 chest X-ray intersections excluded from training, achieving the lowest mean FID-RadImageNet in every intersection. In a blinded reader study of these unseen intersections, two radiologists gave CompDiff the highest mean scores among generators for anatomical realism and agreement with the clinical impression, and selected its images most often as the most realistic. Pretraining with CompDiff images improved downstream classification, while CompDiff audit cohorts reduced estimation error on rare intersections. These findings support compositional demographic conditioning for extending medical image synthesis to underserved populations. Code: https://github.com/mahmoudibrahim98/CompDiff

Mahmoud K. Ibrahim, Bart Elen, Chang Sun +5

Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands · Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands · VITO, Belgium

Healthcare Compositional Image Generation Diffusion-Based Image Generation Medical Image Synthesis Intersectional Fairness

Mar 16, 2026cs.CV

Beyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation

Automated radiology report generation from 3D CT volumes often suffers from incomplete pathology coverage. We provide empirical evidence that this limitation stems from a representational bottleneck: contrastive 3D CT embeddings encode discriminative pathology signals, yet exhibit severe dimensional concentration, with as few as 2 effective dimensions out of 512. Corroborating this, scaling the language model yields no measurable improvement, suggesting that the bottleneck lies in the visual representation rather than the generator. This bottleneck limits both generation and retrieval; naive static retrieval fails to improve clinical efficacy and can even degrade performance. We propose \textbf{AdaRAG-CT}, an adaptive augmentation framework that compensates for this visual bottleneck by introducing supplementary textual information through controlled retrieval and selectively integrating it during generation. On the CT-RATE benchmark, AdaRAG-CT achieves state-of-the-art clinical efficacy, improving Clinical F1 from 0.420 (CT-Agent) to 0.480 (+6 points); ablation studies confirm that both the retrieval and generation components contribute to the improvement. Code is available at https://github.com/renjie-liang/Adaptive-RAG-for-3DCT-Report-Generation.

Renjie Liang, Yiling Ma, Yang Xing +6

University of Florida · Yale University · University of Southern California

Radiology Report Generation Multimodal RAG Retrieval-Augmented Generation Healthcare Adaptive RAG Computed Tomography

Mar 11, 2026cs.CL

Coverage-Controlled Preference Mining from Noisy Claim Verification for Evidence-Grounded Generation

Evidence-grounded generation produces summaries whose claims should be supported by supplied evidence, but claim-level verifiers provide noisy feedback and can reward models that simply say less. We study this problem in clinical Brief Hospital Course summarization, where outputs must remain grounded in patient-specific EHR evidence. We introduce VERI-DPO, a preference-mining framework that converts noisy claim verification into coverage-controlled summary-level preferences. For each evidence-window prompt, VERI-DPO samples multiple candidate summaries, decomposes them into claims, verifies each claim against patient evidence, and forms a preference pair only when the chosen summary has better aggregate verifier-estimated support while retaining comparable verifiable content. Standard Direct Preference Optimization then distills these pairs into a single-sample policy, avoiding inference-time reranking. On patient-disjoint MIMIC-III-Ext-VeriFact-BHC test data, VERI-DPO reduces Not Supported rates from 10.7% to 1.9% under the mining verifier and from 11.6% to 6.4% under a separately prompted GPT-4o judge. In 100 blinded pairwise assessments by two domain researchers, VERI-DPO is preferred over the base model 56 times versus 18 times for factual faithfulness. In a locked zero-shot MIMIC-IV-Ext-BHC transfer test with 1,000 patients and no model adaptation, VERI-DPO lowers Not Supported rates with nearly unchanged scored-claim counts. Multi-seed ablations show that verifier-guided pair construction drives the gains, while coverage and anti-degeneration controls prevent apparent factuality improvements from coming from shorter or less checkable outputs.

Weixin Liu, Congning Ni, Qingyuan Song +4

Vanderbilt University, Nashville, TN, USA · Vanderbilt University Medical Center, Nashville, TN, USA · Virginia Tech, Blacksburg, VA, USA

Claim Verification Healthcare Factual Consistency Evaluation Text Summarization Evidence-Grounded Generation

Mar 5, 2026cs.CL

Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution

Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language models (LLMs) have the potential to automate this task, achieving strong performance requires frontier models such as GPT-5 that are prohibitively expensive to deploy at scale. To efficiently perform biomedical evidence attribution, we present Med-V1, a family of small language models with only three billion parameters. Trained on high-quality synthetic data newly developed in this study, Med-V1 substantially outperforms (+27.0% to +71.3%) its base models on five biomedical benchmarks unified into a verification format. Despite its smaller size, Med-V1 performs comparably to frontier LLMs such as GPT-5, along with high-quality explanations for its predictions. We use Med-V1 to conduct a first-of-its-kind use case study that quantifies hallucinations in LLM-generated answers under different citation instructions. Results show that the format instruction strongly affects citation validity and hallucination, with GPT-5 generating more claims but exhibiting hallucination rates similar to GPT-4o. Additionally, we present a second use case showing that Med-V1 can automatically identify high-stakes evidence misattributions in clinical practice guidelines, revealing potentially negative public health impacts that are otherwise challenging to identify at scale. Overall, Med-V1 provides an efficient and accurate lightweight alternative to frontier LLMs for practical, real-world biomedical evidence attribution. Med-V1 is available at https://github.com/NLM-DIR/Med-V1.

Qiao Jin, Yin Fang, Lauren He +12

Division of Intramural Research, National Library of Medicine, National Institutes of Health · Department of Computer Science, University of Virginia · Center for Cancer Research, National Cancer Institute, National Institutes of Health +3

LLM Answer Verification Healthcare Small Language Models LLM Hallucination Detection Medical LLMs Scientific Claim Verification

Mar 5, 2026cs.CV

LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation

Medical image analysis depends on accurate segmentation and controllable synthesis, but both tasks face severe spatial imbalance: lesions occupy small regions against large backgrounds. We study adaptive spatial weighting as a task-level design principle and instantiate it in two adapters. LAW learns per-pixel loss weights for mask-conditioned diffusion by modulating a ratio prior with a feature-dependent delta map, with normalization, clamping, and Dice regularization for stability. ORDER improves lightweight segmentation by adding selective bidirectional skip attention with stage-wise confidence gating. On held-out diffusion test sets, LAW lowers FID from 158.13

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0.15 to 108.43

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0.71 on Polyps, from 144.13

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0.31 to 89.51

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0.96 on KiTS19, and from 139.22

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0.38 to 112.58

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0.68 on BRISC, while improving held-out mask-recovery Dice from 0.681

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0.013 to 0.825

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0.003 on Polyps. When the resulting images are added to nnUNet training, downstream Polyps mDice rises from 71.7

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0.4 to 74.1

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0.8. On the cleaned Polyps segmentation protocol, the reported ORDER configuration reaches 76.3

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1.9 mDice and 67.2

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2.0 mIoU at 42K parameters and 0.11 GFLOPs, versus 70.3

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1.5 mDice and 59.9

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1.7 mIoU for matched MK-UNet. On BRISC under the same training recipe, ORDER reaches 77.4

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0.8 mDice and 68.1

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0.7 mIoU. These results position adaptive spatial weighting as a practical design idea for both medical diffusion and efficient segmentation.

Anugunj Naman, Ayushman Singh, Gaibo Zhang +1

Elmore Family School of Electrical and Computer Engineering Purdue University, West Lafayette, USA · Sesame AI, New York, USA · Department of Computer Science Purdue University, West Lafayette, USA +1

Image Segmentation Healthcare Adaptive Loss Weighting Diffusion-Based Image Generation Medical Image Segmentation Medical Image Synthesis

Mar 4, 2026cs.CL

MIND: Unified Inquiry and Diagnosis RL with Criteria Grounded Clinical Supports for Psychiatric Consultation

Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when evidence suffices. Existing dialogue and retrieval-augmented systems condition policies on raw histories or appended passages, leaving observed evidence, missing checks, differentials, and support reliability entangled. We introduce MIND, a criteria-grounded evidence-state decision interface. At each turn, MIND constructs a typed state containing observed evidence, unresolved criterion checks, active differentials, criterion-linked supports, and reliability metadata. A training-split Psychiatric Reasoning Bank supplies gated supports, turning retrieval into state construction rather than prompt injection. The same state conditions action selection, process rewards, information-gain scoring, and trajectory rectification. Under EMR-grounded simulator protocols, MIND improves accuracy by 8.8 and 7.3 points over strong inference-only, RAG, and RL baselines, with gains transferring to public MDD-5k dialogues. Matched interventions attribute these gains to the shared state rather than prompt length, retrieval text, or formatting. MIND targets screening-level decision support, not autonomous diagnosis. Code is available at https://github.com/Lingxi-mental-health/MIND.Å

Guoyi Li, Shihao Xu, Jiatong Ma +4

EverMind AI Inc., California, USA · Venus Team, Ant Group · Tianqiao and Chrissy Chen Institute, Shanghai, China +3

Healthcare Evidence-Grounded Reasoning Medical Diagnosis Clinical Decision Support Mental Health RL for Language Model Reasoning+2

Mar 3, 2026cs.CV

BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology

Generalist pathology foundation models (PFMs), pretrained on large-scale multi-organ datasets, have demonstrated remarkable predictive capabilities across diverse clinical applications. However, their proficiency on the full spectrum of clinically essential tasks within a specific organ system remains an open question due to the lack of large-scale validation cohorts for a single organ as well as the absence of a tailored training paradigm that can effectively translate broad histomorphological knowledge into the organ-specific expertise required for specialist-level interpretation. In this study, we propose BRIGHT, the first PFM specifically designed for breast pathology, trained on over 51,000 breast whole-slide images derived from a cohort of over 40,000 patients across 19 hospitals. BRIGHT employs a collaborative generalist-specialist framework to capture both universal and organ-specific features. To comprehensively evaluate the performance of PFMs on breast oncology, we curate the largest multi-institutional cohorts to date for downstream task development and evaluation, comprising over 25,000 WSIs across 10 hospitals. The validation cohorts cover the full spectrum of breast pathology across 25 distinct clinical tasks spanning diagnosis, biomarker prediction, treatment response and survival prediction. Extensive experiments demonstrate that BRIGHT outperforms five leading generalist PFMs, achieving state-of-the-art (SOTA) performance in 25 of 25 internal validation tasks and in 4 of 11 external validation tasks with excellent heatmap interpretability. By evaluating on large-scale validation cohorts, this study not only demonstrates BRIGHT's clinical utility in breast oncology but also validates a collaborative generalist-specialist paradigm, providing a scalable template for developing PFMs on a specific organ system, accelerating the translation of foundation models into ...

Xiaojing Guo, Jiatai Lin, Yumian Jia +39

Breast Cancer Healthcare Pathology Foundation Models Oncology Whole-Slide Image Analysis

Mar 3, 2026cs.CV

Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language

Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data modalities, a single model can be rapidly adapted to address multiple modalities and tasks with minimal supervision. This potential comes with the urgent need to reliably benchmark, understand and compare the performance and clinical impact of foundation models across data modalities and clinical tasks. We introduce UNICORN, a fundamentally new benchmarking concept for medical foundation models. UNICORN brings four main contributions to medical artificial intelligence. First, a framework that enables a one-to-many benchmarking approach, where a single foundation model is tested across multiple tasks and data modalities. Here, we populate it with 20 tasks across radiology, pathology, and clinical text, covering classification, detection, segmentation, regression, and vision-language generation. Second, a publicly available evaluation platform that implements, for the first time, a two-step approach to run foundation models for data encoding followed by custom task-specific adaptation via few-shot learning and linear probing mechanisms. Third, we create a meta-model that combines state-of-the-art foundation models in pathology, radiology and language with novel task-specific adapters that address all UNICORN tasks, which we refer to as Unicorn Model-0 (UM-0). Finally, we design a novel UNICORN score to benchmark and compare model performance across all tasks. We present the results of UM-0 using sequestered test data from over 2,400 patients, 3,700 vision cases, and 2,400 clinical reports from 17 institutions across eight countries, spanning eight anatomical regions and four imaging modalities. Data, baselines, and evaluation platform are publicly accessible at unicorn.grand-challenge.org.

Michelle Stegeman, Lena Philipp, Fennie van der Graaf +20

and on behalf of the UNICORN consortium · Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands · Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands +2

Computational Pathology Medical Imaging Foundation Models Healthcare Multimodal Foundation Models Multimodal Model Evaluation Medical Image Benchmarks

Mar 1, 2026cs.CV

Differential privacy representation geometry for medical image analysis

Differential privacy (DP)'s effect in medical imaging is typically evaluated only through end-to-end performance, leaving the mechanism of privacy-induced utility loss unclear. We introduce Differential Privacy Representation Geometry for Medical Imaging (DP-RGMI), a framework that interprets DP as a structured transformation of representation space and decomposes performance degradation into encoder geometry and task-head utilization. Geometry is quantified by representation displacement from initialization and spectral effective dimension, while utilization is measured as the gap between linear-probe and end-to-end utility. Across over 594,000 images from four chest X-ray datasets and multiple pretrained initializations, we show that DP is consistently associated with a utilization gap even when linear separability is largely preserved. At the same time, displacement and spectral dimension exhibit non-monotonic, initialization- and dataset-dependent reshaping, indicating that DP alters representation anisotropy rather than uniformly collapsing features. Correlation analysis reveals that the association between end-to-end performance and utilization is robust across datasets but can vary by initialization, while geometric quantities capture additional prior- and dataset-conditioned variation. These findings position DP-RGMI as a reproducible framework for diagnosing privacy-induced failure modes and informing privacy model selection.

Soroosh Tayebi Arasteh, Marziyeh Mohammadi, Sven Nebelung +1

Lab for AI in Medicine, RWTH Aachen University, Aachen, Germany · Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany · INDA Institute, RWTH Aachen University, Aachen, Germany

Neural Representation Geometry Healthcare Privacy-Preserving ML Differential Privacy Medical Image Representation Learning

Mar 1, 2026cs.CL

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing systems are either closed-source or computationally prohibitive, precluding the on-premises deployment required for patient privacy and PHI compliance. We introduce MEDGPT-OSS, an open-weight, 20B-parameter generalist vision-language model designed to facilitate open research in clinical AI. Rather than relying on architectural complexity, MEDGPT-OSS pairs the GPT-oss language backbone with a visual front-end via a optimized, three-stage training curriculum. By progressively domain-adapting these modules through rigorous data curation and long-context multimodal alignment, we demonstrate that a 20B model can bridge the capacity gap. It successfully outperforms larger open medical models on out-of-distribution (OOD) multimodal reasoning and complex text-only clinical tasks. By unifying diverse modalities under a single instruction-following interface, MEDGPT-OSS maintains a parameter-efficient footprint fully compatible with commodity GPUs. We release the complete training recipe, open-weight checkpoints, and a rigorous evaluation harness to serve as a verifiable foundation for privacy-preserving, institution-specific clinical AI research.

Kai Zhang, Zhengqing Yuan, Cheng Peng +10

Department of Computer Science and Engineering, Lehigh University · Department of Computer Science and Engineering, University of Notre Dame · Department of Health Outcomes & Biomedical Informatics, University of Florida +3

Healthcare Multimodal Pretraining Medical VLMs Clinical Reasoning Multimodal Foundation Models

Feb 25, 2026cs.LG

HEARTS: Benchmarking LLM Reasoning on Health Time Series

The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of health time series modalities and tasks, failing to reflect the diverse domains and extensive temporal dependencies inherent in real-world physiological modeling. To bridge these gaps, we introduce HEARTS (Health Reasoning over Time Series), a unified benchmark for evaluating hierarchical reasoning capabilities of LLMs over general health time series. HEARTS integrates 16 real-world datasets across 12 health domains and 20 signal modalities, and defines a comprehensive taxonomy of 110 tasks grouped into four core capabilities: Perception, Inference, Generation, and Deduction. Evaluating 16 state-of-the-art LLMs on more than 20K test samples reveals intriguing findings. First, LLMs substantially underperform specialized models, and their performance is only weakly related to general reasoning scores. Moreover, LLMs often rely on simple heuristics and struggle with multi-step temporal reasoning. Finally, performance declines with increasing temporal complexity, with similar failure modes within model families, indicating that scaling alone is insufficient. By making these gaps measurable, HEARTS provides a standardized testbed and living benchmark for developing next-generation LLM agents capable of reasoning over diverse health signals.

Sirui Li, Shuhan Xiao, Mihir Joshi +4

University of California, Los Angeles, 2Google Research · 2Google Research

LLM Evaluation Time Series Reasoning Healthcare

Feb 16, 2026cs.CV

Scalable next-scale autoregression for medical image generation across anatomical regions

Autoregressive pretraining has been key to the scalability of large language models, yet medical generative foundation models remain predominantly based on diffusion. Here we introduce MedVAR, the first foundation model for all-round medical image generation through autoregressive training, and find it offers improved generation quality and efficiency, stronger scalability, and broader adaptability to downstream clinical tasks than diffusion-based models. A tokenizer trained on medical images and separate semantic and structural controls enable generation across six anatomical regions in computed tomography and magnetic resonance imaging. Trained on 438,905 slices from 40 datasets, including seven internal clinical centres, MedVAR generates images 12-19 times faster than 100-step diffusion baselines. Generation quality improves with model size. Pretraining on generated images improves seven-centre hepatocellular carcinoma segmentation Dice from 0.615 to 0.632, while reconstruction using MedVAR images approaches the volumetric segmentation performance of fully sampled volumes. Membership inference reaches 3.54% sensitivity at a 1% false-positive rate, while copy detection performs near chance. These findings establish next-scale autoregression as a scalable and versatile approach to medical image generation and downstream analysis.

Zhicheng He, Yunpeng Zhao, Junde Wu +7

National University of Singapore · University of Oxford · Zhejiang University +1

Medical Imaging Foundation Models Healthcare Visual Autoregressive Models Autoregressive Image Generation Medical Image Synthesis

Feb 11, 2026cs.CL

A Patient Simulation Framework for Risk Assessment of Conversational Healthcare AI: Evaluation of an Antidepressant Decision Aid

Objective: This study develops and validates a patient simulation framework that aligns with the National Institute of Standards and Technology AI Risk Management Framework MAP and MEASURE functions, providing an empirical basis for identifying and characterizing performance risks in conversational clinical AI across medical, linguistic, and behavioral patient variation. We applied the framework to a conversational decision aid for antidepressant selection in major depressive disorder. Methods: The simulator integrates three profile dimensions: (1) medical profiles constructed from All of Us electronic health records using risk-ratio gating; (2) linguistic profiles modeling a health literacy gradient and condition-specific communication; and (3) behavioral profiles representing cooperative, distracted, and adversarial engagement. We generated 500 simulated conversations and evaluated profile fidelity through human annotation and a large language model (LLM) judge, then assessed downstream effects on the AI Decision Aid's concept retrieval and antidepressant recommendations. Results: The patient simulator expressed medical concepts with high fidelity (96.6% accurate across 8,210 concepts), with human inter-annotator agreement of 0.73

κ

and LLM-judge agreement against human annotators of 0.78

κ

, both indicating substantial agreement. Behavioral profiles were reliably distinguished (0.93

κ

, near perfect agreement), and linguistic profiles showed substantial agreement at the lower bound of the substantial range (0.61

κ

), adequate to support profile-level analysis. The framework revealed monotonic degradation in AI Decision Aid performance across the health literacy gradient. Rank-1 concept retrieval increased from 47.6% for limited health literacy to 81.9% for proficient health literacy, with corresponding declines in antidepressant recommendation accuracy.

Md Tanvir Rouf Shawon, Mohammad Sabik Irbaz, Hadeel R. A. Elyazori +6

Department of Computer Science, George Mason University, Fairfax, VA, United States · Department of Information Sciences and Technology, George Mason University, Fairfax, VA, United States · Department of Health Administration and Policy, George Mason University, Fairfax, VA, United States +1

AI Risk Management Healthcare Clinical Decision Support

Feb 11, 2026cs.LG

Capture Timing-Attention of Events in Clinical Time Series

The contemporary paradigm of trajectory learning operates fundamentally at the level of group dynamics, systematically reducing individual-level complexity to fit group-level models, thus rendering effective patient subtyping difficult and individual-level modeling largely out of reach. We propose a data-driven paradigm that introduces a dedicated individual-level temporal variable to capture \emph{Timing Attention} (i.e., the degree of concentration of an event's timing distribution across the patient cohort), thereby rendering timing a \emph{computable dimension} that enables individualized temporal features in trajectory learning. Instantiated as the Level-of-Individual Time Transformation (LITT) and applied to longitudinal EHR data from 3,276 breast cancer patients, the proposed paradigm demonstrates, for the first time to our knowledge: (1) automatic discovery of clinically significant patient trajectories, and (2) counterfactual timing deduction, that is, a \emph{What-If Machine}. Both results are purely data-driven, requiring no prior domain knowledge. LITT further achieves strong performance on timing prediction and survival analysis tasks.

Jia Li, Yu Hou, Rui Zhang

Department of Surgery; Department of Computer Science, U of M Minneapolis, MN, USA

Event Time Prediction Healthcare Counterfactual Prediction Longitudinal EHR Modeling

Feb 6, 2026cs.CL

An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models

Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requires substantial data and expert annotation. We developed ClinMPO, an evidence-guided reinforcement-learning framework guided by the psychiatrist-defined Clinical Psychiatry Thinking Strategy (CPTS). ClinMPO uses ClinRM, a reward model trained on 18,569 question--answer pairs from 4,474 psychiatry articles. We evaluated four Qwen3 sizes on 1,737 model-screened questions. ClinMPO outperformed Base, supervised fine-tuning and standard group relative policy optimization across scales. From responses by 300 senior pre-licensure medical students, we established the human baseline, a medical-student reference. The 4B model approached this baseline, whereas the 8B model surpassed it and ranked first among 31 models and post-training variants. ClinMPO improved performance across two complementary schemes covering ICD-11 diagnostic categories and psychiatric practice competencies. Blinded assessment by three clinicians showed improved rationale quality across CPTS criteria. These findings highlight how existing clinical evidence and specialist knowledge can be incorporated into the development of medical AI systems through evidence-guided learning.

Xinxin Lin, Guangxin Dai, Yi Zhong +25

The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China · Shandong University, Jinan, China · Peking University Sixth Hospital, Beijing, China +6

Healthcare Mental Health RL for Language Model Reasoning Small Language Models Medical Reasoning Medical LLMs

Feb 4, 2026cs.SD

RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity

As conversational multimodal AI tools are increasingly adopted to process patient data for health assessment, robust benchmarks are needed to measure progress and expose failure modes under realistic conditions. Despite the importance of respiratory audio for mobile health screening, respiratory audio question answering remains underexplored, with existing studies evaluated narrowly and lacking real-world heterogeneity across modalities, devices, and question types. We hence introduce the \textbf{Respiratory-Audio Question-Answering (RA-QA) benchmark}, including a standardized data generation pipeline, a comprehensive multimodal QA collection, and a unified evaluation protocol. RA-QA harmonizes public RA datasets into a collection of 9 million format-diverse QA pairs covering diagnostic and contextual attributes. We benchmark general audio-language models as well as domain-specific architectures, establishing reproducible reference points and showing how current approaches fail under heterogeneity.

Gaia A. Bertolino, Yuwei Zhang, Tong Xia +2

University of Cambridge · Cambridge, United Kingdom · Tsinghua University +3

Audio-Language Model Evaluation Audio QA Healthcare Audio Understanding Multimodal QA Audio-Language Models+2

Feb 2, 2026cs.AI

Think Like a Doctor: Conversational Diagnosis through the Exploration of Diagnostic Knowledge Graphs

Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incomplete information. Existing approaches often rely on the parametric knowledge of a model or assume that patients provide rich and concrete information, which is unrealistic. To address these limitations, we propose a conversational diagnosis system that explores a diagnostic knowledge graph to reason in two steps: (i) generating diagnostic hypotheses from the dialogue context, and (ii) verifying hypotheses through clarifying questions, which are repeated until a final diagnosis is reached. Since evaluating the system requires a realistic patient simulator that responds to the system's questions, we adopt PatientSim, a persona-driven patient simulator, together with patient profiles from MIMIC-IV. We further adapt it with low-specificity symptom reporting to reflect how real-world patients describe symptoms vaguely during early clinical encounters. Experiments show improved diagnostic accuracy and efficiency over strong baselines, and physician evaluations support the realism of our simulator and the clinical utility of the generated clarifying questions. Our code will be released upon publication.

Jeongmoon Won, Seungwon Kook, Yohan Jo

Graduate School of Data Science, Seoul National University

Healthcare Medical Diagnosis Clinical Reasoning Differential Diagnosis