搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. Systematic reviews (SR), in which experts summarize and analyze evidence across individual studies to provide insights on a specialized topic, are a cornerstone for evidence-based clinical decision-making, research, and policy. Given the exponential growth of scientific articles, there is growing interest in using large language models (LLMs) to automate SR generation. However, the ability of LLMs to critically assess evidence and reason across multiple documents to provide recommendations at the same proficiency as domain experts remains poorly characterized. We therefore ask: **Can LLMs match the conclusions of systematic reviews written by clinical experts when given access to the same studies?** To explore this question, we present MedEvidence, a benchmark pairing findings from 100 medical SRs with the studies they are based on.

ICLR 2026 Poster accepted paper at ICLR 2026. Effective information seeking in multi-turn medical dialogues is critical for accurate diagnosis, especially when dealing with incomplete information. Aligning Large Language Models (LLMs) for these interactive scenarios is challenging due to the uncertainty inherent in user-agent interactions, which we formulate as a Hierarchical Markov Decision Process (H-MDP). While conventional Reinforcement Learning (RL) methods like Group Relative Policy Optimization (GRPO) struggle with long-horizon credit assignment and Proximal Policy Optimization (PPO) suffers from unstable value estimation in this context, we propose a novel uncertainty-aware Adaptive Tree Policy Optimization (ATPO) algorithm. Our method adaptively allocates the rollout budget to states with high uncertainty, quantified by a composite metric of Bellman error and action-value variance.

ICLR 2026 Poster accepted paper at ICLR 2026. In precision medicine, quantitative multi-omic features, topological context, and textual biological knowledge play vital roles in identifying disease-critical signaling pathways and targets, guiding the discovery of novel therapeutics and effective treatment strategies. Existing pipelines capture only one or two of these—numerical omics ignore topological context, text-centric LLMs lack quantitative grounded reasoning, and graph-only models underuse rich node semantics and the generalization power of LLMs—thereby limiting mechanistic interpretability. Although Process Reward Models (PRMs) aim to guide reasoning in LLMs, they remain limited by coarse step definitions, unreliable intermediate evaluation, and vulnerability to reward hacking with added computational cost. These gaps motivate jointly integrating quantitative multi-omic signals, topological structure with node annotations, and literature-scale text via LLMs, using subgraph reasoning as the principle bridge linking numeric evidence, topological knowledge and language context.

ICLR 2026 Poster accepted paper at ICLR 2026. The professionalism of a human doctor in outpatient service depends on two core abilities: the ability to make accurate medical decisions and the medical consultation skill to conduct strategic, empathetic patient inquiry. Existing Large Language Models (LLMs) have achieved remarkable accuracy on medical decision-making benchmarks. However, they often lack the ability to conduct the strategic and empathetic consultation, which is essential for real-world clinical scenarios. To address this gap, we propose Doctor-R1, an AI doctor agent trained to master both of the capabilities by ask high-yield questions and conduct strategic multi-turn inquiry to guide decision-making.

ICLR 2026 Poster accepted paper at ICLR 2026. Segmentation in pathology image requires handling out-of-domain tissue morphologies and new pathologies beyond training distributions, where traditional closed-set segmentation approaches fail to generalize. Reasoning segmentation enables zero-shot generalization via prompting with text queries. However, existing reasoning segmentation models face three barriers when applied to pathology: (1) the vision encoder lack pathology-specific knowledge and robustness to staining variations, (2) the large language model (LLM) backbone for reasoning fails to identify whether it has gathered sufficient semantic context to trigger the segmentation output, and (3) no reasoning segmentation benchmarks and datasets exist for pathology analysis. Consequently, we introduce PathChat-SegR1, a reasoning segmentation model built upon pathology-specific vision encoders trained with a novel stain-invariant self-distillation for robust pathology image representations.

ICLR 2026 Poster accepted paper at ICLR 2026. Clinical decision-making is a dynamic, interactive, and cyclic process where doctors have to repeatedly decide on which clinical action to perform and consider newly uncovered information for diagnosis and treatment. Large Language Models (LLMs) have the potential to support clinicians in this process, however, most applications of LLMs in clinical decision support suffer from one of two limitations: Either they assume the unrealistic scenario of immediate availability of all patient information and do not model the interactive and iterative investigation process, or they restrict themselves to the limited "out-of-the-box" capabilities of large pre-trained models without performing task-specific training. In contrast to this, we propose to model clinical decision-making for diagnosis with a hypothesis-driven uncertainty-aware language agent, LA-CDM, that converges towards a diagnosis via repeatedly requesting and interpreting relevant tests. Using a hybrid training paradigm combining supervised and reinforcement learning, we train LA-CDM with three objectives targeting critical aspects of clinical decision-making: accurate hypothesis generation, hypothesis uncertainty estimation, and efficient decision-making. Code/project link: https://github.com/dharouni/LA-CDM

ICLR 2026 Poster accepted paper at ICLR 2026. Despite the impressive performance of LLM-powered agents, their adoption for Electronic Health Record (EHR) data access remains limited by the absence of benchmarks that adequately capture real-world clinical data access flows. In practice, two core challenges hinder deployment: query ambiguity from vague user questions and value mismatch between user terminology and database entries. To address this, we introduce EHR-ChatQA, an interactive database question answering benchmark that evaluates the end-to-end workflow of database agents: clarifying user questions, using tools to resolve value mismatches, and generating correct SQL to deliver accurate answers. To cover diverse patterns of query ambiguity and value mismatch, EHR-ChatQA assesses agents in a simulated environment with an LLM-based user across two interaction flows: Incremental Query Refinement (IncreQA), where users add constraints to existing queries, and Adaptive Query Refinement (AdaptQA), where users adjust their search goals mid-conversation. Code/project link: https://github.com/glee4810/EHR-ChatQA

ICLR 2026 Poster accepted paper at ICLR 2026. The application of large language models (LLMs) in the medical field has garnered significant attention, yet their reasoning capabilities in more specialized domains like anesthesiology remain underexplored. To bridge this gap, we introduce AnesSuite, the first comprehensive dataset suite specifically designed for anesthesiology reasoning in LLMs. The suite features AnesBench, an evaluation benchmark tailored to assess anesthesiology-related reasoning across three levels: factual retrieval (System 1), hybrid reasoning (System 1.x), and complex decision-making (System 2). Alongside this benchmark, the suite includes three training datasets that provide an infrastructure for continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning with verifiable rewards (RLVR). Code/project link: https://github.com/MiliLab/AnesSuite

ICLR 2026 Poster accepted paper at ICLR 2026. Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms discards transient acoustic events and clinical context; (ii) limited data availability, exacerbated by severe class imbalance. To bridge these gaps, we present **_Resp-Agent_**, an autonomous multimodal system orchestrated by a novel Active Adversarial Curriculum Agent (Thinker-A²CA). Unlike static pipelines, Thinker-A²CA serves as a central controller that actively identifies diagnostic weaknesses and schedules targeted synthesis in a closed loop. To address the representation gap, we introduce a modality-weaving Diagnoser that weaves clinical text with audio tokens via strategic global attention and sparse audio anchors, capturing both long-range clinical context and millisecond-level transients. Code/project link: https://github.com/zpforlove/Resp-Agent

ICLR 2026 Poster accepted paper at ICLR 2026. Medical Vision-Language Models (VLMs) hold immense promise for complex clinical tasks, but their reasoning capabilities are often constrained by text-only paradigms that fail to ground inferences in visual evidence. This limitation not only curtails performance on tasks requiring fine-grained visual analysis but also introduces risks of visual hallucination in safety-critical applications. Thus, we introduce MedVR, a novel reinforcement learning framework that enables annotation-free visual reasoning for medical VLMs. Its core innovation lies in two synergistic mechanisms: Entropy-guided Visual Regrounding (EVR) uses model uncertainty to direct exploration, while Consensus-based Credit Assignment (CCA) distills pseudo-supervision from rollout agreement.

ICLR 2026 Poster accepted paper at ICLR 2026. Pan-cancer screening in large-scale CT scans remains challenging for existing AI methods, primarily due to the difficulty of localizing diverse types of tiny lesions in large CT volumes. The extreme foreground-background imbalance significantly hinders models from focusing on diseased regions, while redundant focus on healthy regions not only decreases the efficiency but also increases false positives. Inspired by radiologists' glance and focus diagnostic strategy, we introduce GF-Screen, a Glance and Focus reinforcement learning framework for pan-cancer screening. GF-Screen employs a Glance model to localize the diseased regions and a Focus model to precisely segment the lesions, where segmentation results of the Focus model are leveraged to reward the Glance model via Reinforcement Learning (RL). Code/project link: https://github.com/Luffy03/GF-Screen

ICLR 2026 Poster accepted paper at ICLR 2026. In clinical practice, physicians refrain from making decisions when patient information is insufficient. This behavior, known as abstention, is a critical safety mechanism preventing potentially harmful misdiagnoses. Recent investigations have reported the application of large language models (LLMs) in medical scenarios. However, existing LLMs struggle with the abstentions, frequently providing overconfident responses despite incomplete information. This limitation stems from conventional abstention methods relying solely on model self-assessments, which lack systematic strategies to identify knowledge boundaries with external medical evidences.

ICLR 2026 Poster accepted paper at ICLR 2026. Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to individual tasks, modalities, or organs. This fragmentation contrasts sharply with clinical practice, where experts seamlessly integrate diverse knowledge: anatomical priors from training, exemplar-based reasoning from reference cases, and iterative refinement through real-time interaction. We present $\textbf{K-Prism}$, a unified segmentation framework that mirrors this clinical flexibility by systematically integrating three knowledge paradigms: (i) $\textit{semantic priors}$ learned from annotated datasets, (ii) $\textit{in-context knowledge}$ from few-shot reference examples, and (iii) $\textit{interactive feedback}$ from user inputs like clicks or scribbles. Code/project link: https://github.com/bangwayne/K-Prism

ICLR 2026 Poster accepted paper at ICLR 2026. Large visual language models (VLMs) have shown strong multi-modal medical reasoning ability, but most operate as end-to-end black boxes, diverging from clinicians’ evidence-based, staged workflows and hindering clinical accountability. Complementarily, expert visual grounding models can accurately localize regions of interest (ROIs), providing explicit, reliable evidence that improves both reasoning accuracy and trust. In this paper, we introduce **CARE**, advancing **C**linical **A**ccountability in multi-modal medical **R**easoning with an **E**vidence-grounded agentic framework. Unlike existing approaches that couple grounding and reasoning within a single generalist model, CARE decomposes the task into coordinated sub-modules to reduce shortcut learning and hallucination: a compact VLM proposes relevant medical entities; an expert entity-referring segmentation model produces pixel-level ROI evidence; and a grounded VLM reasons over the full image augmented by ROI hints.

ICLR 2026 Poster accepted paper at ICLR 2026. Cancer patients are increasingly turning to large language models (LLMs) for medical information, making it critical to assess how well these models handle complex, personalized questions. However, current medical benchmarks focus on medical exams or consumer-searched questions and do not evaluate LLMs on real patient questions with patient details. In this paper, we first have three hematology-oncology physicians evaluate cancer-related questions drawn from real patients. While LLM responses are generally accurate, the models frequently fail to recognize or address false presuppositions} in the questions, posing risks to safe medical decision-making.

ICLR 2026 Poster accepted paper at ICLR 2026. Modern clinical diagnosis relies on the comprehensive analysis of multi-modal patient data, drawing on medical expertise to ensure systematic and rigorous reasoning. Recent advances in Vision–Language Models (VLMs) and agent-based methods are reshaping medical diagnosis by effectively integrating multi-modal information. However, they often output direct answers and empirical-driven conclusions without clinical evidence supported by quantitative analysis, which compromises their reliability and hinders clinical usability. Here we propose MedAgent-Pro, an agentic reasoning paradigm that mirrors modern diagnosis principles via a hierarchical diagnostic workflow, consisting of disease-level standardized plan generation and patient-level personalized step-by-step reasoning.

ICLR 2026 Poster accepted paper at ICLR 2026. Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical record (EMR) construction and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using a pipeline that ensures clinical relevance and diversity. Our multi-agent framework transfers the clinical interview protocol into a hierarchical state machine and context tree, supporting over 130 diagnostic states while maintaining clinical standards.

cMedQA2 is an updated Chinese community medical question answering dataset for question-answer matching and medical QA research. It is useful for training and evaluating Chinese medical retrieval, ranking, and answer selection models.

CMCQA is a large Chinese medical conversational question-answering dataset released with knowledge-grounded medical dialogue research. It supports medical conversation QA, knowledge-grounded response generation, and evaluation of Chinese medical dialogue systems.

HuatuoGPT2-SFT-GPT4-140K is a Chinese medical supervised fine-tuning dataset containing medical instruction-style conversations and GPT-4-assisted responses. It is useful for Chinese medical assistant alignment and medical LLM instruction tuning.

Huatuo-26M is a large-scale Chinese medical question-answering dataset with about 26 million QA pairs collected for medical language modeling and medical dialogue research. It is suitable for Chinese medical LLM pretraining, fine-tuning, and QA system development.

MedQA is a medical examination question answering benchmark with English and Chinese medical licensing-style question sets, including mainland China and Taiwan variants. It is widely used for medical QA and medical reasoning evaluation.

MedDG is an entity-centric Chinese medical consultation dataset with domain entity annotations for medical dialogue generation. It supports entity-aware response generation, medical consultation modeling, and dialogue systems that ground responses in clinical concepts.

CMB is a comprehensive Chinese medical benchmark for evaluating medical large language models on medical exams, reasoning, and clinical knowledge questions. It is suited for Chinese medical QA, LLM evaluation, and instruction-following assessment.

国家医疗保障局公告的 2026 全国医学模拟人和健康传感器智能感知大赛，由国家医保局与湖南省人民政府联合举办，赛道覆盖 AI 虚拟病人、虚拟病人对话模拟、虚拟手术/诊疗系统、护理具身机器人和多类健康传感器智能感知设备。公告要求 2026-05-31 前报名、2026-06-10 前上传参赛项目信息。

京东健康全球医疗 AI 创新大赛公开页面显示赛事聚焦睡眠监测智能算法与医疗大模型创新应用两个方向，面向全球高校、科研机构、企业和个人开放报名，赛程含 6.17-8.7 初赛、后续复赛和 9.21 决赛。

This Nature Portfolio / npj Health Systems collection is open for submissions until 2026-06-15. It focuses on software-powered healthcare, including clinical decision-making and AI integration, EHR and health-data infrastructures, telemedicine, remote care, multimodal agentic AI, trustworthiness, responsibility, alignment, and health-system workflows.

This Nature Portfolio / npj Digital Medicine collection is open for submissions until 2027-03-24. It seeks clinical AI studies centered on pre-specified actions, including pragmatic trials, cluster-randomized and stepped-wedge designs, generative AI in workflow, causal analyses, target trial emulation, post-market surveillance, drift monitoring, safety, economic evaluation, and lifecycle governance.

This Nature Portfolio / npj Digital Medicine collection is open for submissions until 2027-04-30. It invites work at the intersection of computational drug repurposing, multimodal biomedical data, and AI, including omics, EHRs, real-world evidence, imaging, digital phenotyping, LLMs, graph neural networks, multimodal transformers, knowledge graphs, generative AI, causal inference, explainability, and clinical translation.

This Nature Portfolio / npj Digital Medicine collection is open for submissions until 2026-06-01. It focuses on AI tools that support or deliver therapeutic interventions in mental health, including generative AI therapy bots, reinforcement-learning agents, human-in-the-loop models, clinical validity, safety, ethics, equity, and regulation.

This Nature Portfolio / npj Digital Medicine collection is open for submissions until 2026-07-12. It calls for work on agentic AI in care delivery, including real-time evidence-based decision support, virtual and remote patient care, multimodal and longitudinal clinical data, EHRs, medical imaging, genomics, resource-limited deployment, ethics, regulation, quality, and patient safety.

This SAGE / Design for Augmented Humanity special issue calls for papers on human-centred and augmented healthcare AI, including theories, applications, and methodologies. The CFP lists a paper submission deadline of 2026-05-30 and is relevant to clinical translation, trustworthy and explainable medical AI, human-AI collaboration, and clinical workflow AI.

This Applied Sciences special issue calls for novel approaches for machine learning in healthcare applications, with keywords including machine learning, deep learning, explainable AI, generative AI, BERT, GPT, and reinforcement learning. The page lists a manuscript submission deadline of 2026-06-30.

CCF-Deadlines lists PRICAI 2026 with papers due 2026-06-13 UTC-12 and conference dates 2026-11-17 to 2026-11-20 in Guangzhou. PRICAI is a general AI venue relevant to medical AI methods, clinical decision support, medical agents, and trustworthy healthcare AI.

CCF-Deadlines lists NLPCC 2026 with papers due 2026-05-26 UTC+8 and conference dates 2026-11-03 to 2026-11-05 in Macau. NLPCC is relevant to Chinese clinical NLP, Chinese biomedical language resources, medical text mining, and healthcare question answering.

CCF-Deadlines lists ISWC 2026 with full papers due 2026-05-07 AoE, after an abstract deadline on 2026-05-02, and conference dates 2026-10-25 to 2026-10-29 in Bari. ISWC is relevant to medical AI through biomedical knowledge graphs, clinical ontologies, semantic data integration, and explainable clinical decision support.

CCF-Deadlines lists ICTAI 2026 with papers due 2026-06-30 AoE and conference dates 2026-11-02 to 2026-11-04 in Boca Raton. ICTAI is relevant to medical AI tools, clinical decision-support systems, medical LLM agents, and deployable AI techniques for healthcare workflows.

CCF-Deadlines lists EMNLP 2026 with papers due 2026-05-25 UTC-12 and conference dates 2026-10-24 to 2026-10-29 in Budapest. EMNLP is relevant to clinical NLP, biomedical language models, medical text mining, EHR note understanding, and safe medical LLM evaluation.

Stanford AI in Healthcare Leadership and Strategy is a four-week hybrid program for healthcare leaders. It covers AI strategy, governance, model performance and safety, clinical and operational value, implementation, change management, and responsible scaling of AI solutions in healthcare.

This Microsoft Learn module helps healthcare professionals adopt Microsoft AI solutions. It covers goals and challenges in life sciences, pharmacology, and healthcare; opportunities for AI; healthcare provider use cases; and business value from AI in patient recovery and health operations.

Harvard Medical School's AI in Clinical Medicine is an online live course for clinicians and allied health professionals. It covers practical AI skills, real-world case studies, clinical integration challenges, ethical considerations, bias, decision-making, and AI regulation.