搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. Current methods for detecting spurious correlations rely on data splits or error patterns, leaving many harmful shortcuts invisible when counterexamples are absent. We introduce BEE (Bridging Explainability and Embeddings), a framework that shifts the focus from model predictions to the weight space and embedding geometry underlying decisions. By analyzing how fine-tuning perturbs pretrained representations, BEE uncovers spurious correlations that remain hidden from conventional evaluation pipelines. We use linear probing as a transparent diagnostic lens, revealing spurious features that not only persist after full fine-tuning but also transfer across diverse state-of-the-art models. Code/project link: https://github.com/bit-ml/bee

ICLR 2026 Poster accepted paper at ICLR 2026. Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote sensing. However, variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies, leading to camera-specific models with limited generalizability and inadequate cross-camera applicability. To address this bottleneck, we introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities. To enable the conversion of a spectral image with any channel dimensionality to a camera-agnostic representation, we introduce a novel spectral encoder, featuring a self-attention-cross-attention mechanism, to distill salient spectral information into learned spectral representations. Code/project link: https://github.com/IMSY-DKFZ/CARL

ICLR 2026 Poster accepted paper at ICLR 2026. DNA methylation (DNAm) is a key epigenetic modification that regulates gene expression and is pivotal in development and disease. However, profiling DNAm at genome scale is challenging: of $\textasciitilde$28 million CpG sites in the human genome, only about 1–3\% are typically assayed in common datasets due to technological limitations and cost. Recent deep learning approaches, including masking-based generative Transformer models, have shown promise in capturing DNAm–gene expression relationships, but they rely on partially observed DNAm values for unmeasured CpGs and cannot be applied to completely unmeasured samples. To overcome this barrier, we introduce MethylProphet, a gene-guided, context-aware Transformer model for whole-genome DNAm inference without any measured DNAm input.

ICLR 2026 Poster accepted paper at ICLR 2026. This paper aims to develop a lower prediction bound (LPB) for survival time across different treatments in the general right-censored setting. Although previous methods have utilized conformal prediction to construct the LPB, their resulting prediction sets provide only probably approximately correct (PAC)–type miscoverage guarantees rather than exact ones. To address this problem, we propose a new calibration procedure under the potential outcome framework. Under the strong ignorability assumption, we propose a reweighting scheme that can transform the problem into a weighted conformal inference problem, allowing an LPB to be obtained via quantile regression with an exact miscoverage guarantee.

ICLR 2026 Poster accepted paper at ICLR 2026. Real-world temporal data often consists of multiple signal types recorded at irregular, asynchronous intervals. For instance, in the medical domain, different types of blood tests can be measured at different times and frequencies, resulting in fragmented and unevenly scattered temporal data. Similar issues of irregular sampling occur in other domains, such as the monitoring of large systems using event log files. Effectively learning from such data requires handling sets of temporal sparse and heterogeneous signals. In this work, we propose Super Mixing Additive Networks (SuperMAN), a novel and interpretable-by-design framework for learning directly from such heterogeneous signals, by modeling them as sets of implicit graphs.

ICLR 2026 Oral accepted paper at ICLR 2026. In this work, we propose FFDP, a set of IO-aware non-GEMM fused kernels supplemented with a distributed framework for image registration at unprecedented scales. Image registration is an inverse problem fundamental to biomedical and life sciences, but algorithms have not scaled in tandem with image acquisition capabilities. Our framework complements existing model parallelism techniques proposed for large-scale transformer training by optimizing non-GEMM bottlenecks and enabling convolution-aware tensor sharding. We demonstrate unprecedented capabilities by performing multimodal registration of a 100μm ex-vivo human brain MRI volume at native resolution – an inverse problem more than 570× larger than a standard clinical datum in about a minute using only 8 A6000 GPUs.

ICLR 2026 Poster accepted paper at ICLR 2026. Prior-data fitted networks (PFNs) have recently been proposed as a promising way to train tabular foundation models. PFNs are transformers that are pre-trained on synthetic data generated from a prespecified prior distribution and that enable Bayesian inference through in-context learning. In this paper, we introduce CausalFM, a comprehensive framework for training PFN-based foundation models in various causal inference settings. First, we formalize the construction of Bayesian priors for causal inference based on structural causal models (SCMs) in a principled way and derive necessary criteria for the validity of such priors. Building on this, we propose a novel family of prior distributions using causality-inspired Bayesian neural networks that enable CausalFM to perform Bayesian causal inference in various settings, including for back-door, front-door, and instrumental variable adjustment.

ICLR 2026 Poster accepted paper at ICLR 2026. Anomaly detection is a critical task in computer vision with profound implications for medical imaging, where identifying pathologies early can directly impact patient outcomes. While recent unsupervised anomaly detection approaches show promise, they require substantial normal training data and struggle to generalize across anatomical contexts. We introduce D$^2$4FAD, a novel dual distillation framework for few-shot anomaly detection that identifies anomalies in previously unseen tasks using only a small number of normal reference images. Our approach leverages a pre-trained encoder as a teacher network to extract multi-scale features from both support and query images, while a student decoder learns to distill knowledge from the teacher on query images and self-distill on support images. Code/project link: https://github.com/ttttqz/D24FAD

ICLR 2026 Poster accepted paper at ICLR 2026. Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to give to a cancer patient). However, predicting potential out- comes over long horizons is notoriously difficult. Existing methods that break the curse of the horizon typically lack strong theoretical guarantees such as orthogonality and quasi-oracle efficiency. In this paper, we revisit the problem of predicting individualized potential outcomes in sequential decision-making (i.e., estimating Q-functions in Markov decision processes with observational data) through a causal inference lens.

ICLR 2026 Poster accepted paper at ICLR 2026. Hybrid neural ordinary differential equations (neural ODEs) integrate mechanistic models with neural ODEs, offering strong inductive bias and flexibility, and are particularly advantageous in data-scarce healthcare settings. However, excessive latent states and interactions from mechanistic models can lead to training inefficiency and over-fitting, limiting practical effectiveness of hybrid neural ODEs. In response, we propose a new hybrid pipeline for automatic state selection and structure optimization in mechanistic neural ODEs, combining domain-informed graph modifications with data-driven regularization to sparsify the model for improving predictive performance and stability while retaining mechanistic plausibility. Experiments on synthetic and real-world data show improved predictive performance and robustness with desired sparsity, establishing an effective solution for hybrid model reduction in healthcare applications.

ICLR 2026 Poster accepted paper at ICLR 2026. Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, particularly in clinical domains such as intracranial electroencephalography (iEEG), where channel setups vary widely across subjects. In this work, we introduce multi-variate parallel attention (MVPA), a novel self-attention mechanism that disentangles content, temporal, and spatial attention, enabling flexible, generalizable, and efficient modeling of time-series data with varying channel counts and configurations. We use MVPA to build MVPFormer, a generative foundation model for human electrophysiology, trained to predict the evolution of iEEG signals across diverse subjects. To support this and future efforts by the community, we release the SWEC iEEG dataset, the largest publicly available iEEG dataset to date, comprising nearly 10,000 hours of recordings from heterogeneous clinical sources. Code/project link: https://github.com/IBM/multi-variate-parallel-transformer; https://huggingface.co/datasets/NeuroTec/SWEC_iEEG_Dataset

ICLR 2026 Poster accepted paper at ICLR 2026. Ultrasound imaging is widely used due to its safety, affordability, and real-time capabilities, but its 2D interpretation is highly operator-dependent, leading to variability and increased cognitive demand. We present $\textbf{UltraGauss}$: an ultrasound-specific Gaussian Splatting framework that serves as an efficient approximation to acoustic image formation. Unlike projection-based splatting, UltraGauss renders by $\textit{probe-plane intersection}$ with in-plane aggregation, aligning with plane-based echo sampling while remaining fast and memory-efficient. A stable parameterisation and compute-aware GPU rasterisation make this method practical at scale. Code/project link: https://www.robots.ox.ac.uk/~vgg/research/UltraGauss/

ICLR 2026 Poster accepted paper at ICLR 2026. Estimating heterogeneous treatment effects (HTEs) in time-varying settings is particularly challenging, as the probability of observing certain treatment sequences decreases exponentially with longer prediction horizons. Thus, the observed data contain little support for many plausible treatment sequences, which creates severe overlap problems. Existing meta-learners for the time-varying setting typically assume adequate treatment overlap, and thus suffer from exploding estimation variance when the overlap is low. To address this problem, we introduce a novel overlap-weighted orthogonal WO meta-learner for estimating HTEs that targets regions in the observed data with high probability of receiving the interventional treatment sequences.

ICLR 2026 Poster accepted paper at ICLR 2026. Modeling the temporal dynamics of the human brain remains a core challenge in computational neuroscience and artificial intelligence. Traditional methods often ignore the biological spike characteristics of brain activity and find it difficult to reveal the dynamic dependencies and causal interactions between brain regions, limiting their effectiveness in brain function research and clinical applications. To address this issue, we propose a Spike-based Digital Brain (Spike-DB), a novel fundamental model that introduces the spike computing paradigm into brain time series modeling. Spike-DB encodes fMRI signals as spike trains and learns the temporal driving relationships between anchor and target regions to achieve high-precision prediction of brain activity and reveal underlying causal dependencies and dynamic relationship characteristics. Code/project link: https://github.com/UAIBC-Brain/Spike-DB

ICLR 2026 Poster accepted paper at ICLR 2026. Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-making. Yet, the survival analysis setting poses unique challenges for HTE estimation due to censoring, unobserved counterfactuals, and complex identification assumptions. Despite recent advances, from causal survival forests to survival meta-learners and outcome imputation approaches, evaluation practices remain fragmented and inconsistent. We introduce SurvHTE‐Bench, the first comprehensive benchmark for HTE estimation with censored outcomes. The benchmark spans (i) a modular suite of synthetic datasets with known ground truth, systematically varying causal assumptions and survival dynamics, (ii) semi-synthetic datasets that pair real-world covariates with simulated treatments and outcomes, and (iii) real-world datasets from a twin study (with known ground truth) and from an HIV clinical trial.

ICLR 2026 Poster accepted paper at ICLR 2026. Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for this task fail to properly adjust for time-varying confounding and thus yield biased estimates. There are only a few neural methods with proper adjustments, but these have inherent limitations (e.g., division by propensity scores that are often close to zero), which result in poor performance. As a remedy, we introduce the iterative G-computation network (IGC-Net). Our IGC-Net is a novel, neural end-to-end model which adjusts for time-varying confounding in order to estimate conditional average potential outcomes (CAPOs) over time.

ICLR 2026 Poster accepted paper at ICLR 2026. Healthcare insurance fraud detection presents unique machine learning challenges: labeled data are scarce due to delayed verification processes, and fraudulent behaviors evolve rapidly, often manifesting in complex, graph-structured interactions. Existing methods struggle in such settings. Pretraining routines typically overlook structural anomalies under limited supervision, while online models often fail to adapt to changing fraud patterns without labeled updates. To address these issues, we propose the Continual Fiedler Vector Graph model (ConFVG), a fraud detection framework designed for label-scarce and non-stationary environments.

ICLR 2026 Poster accepted paper at ICLR 2026. Accurate detection of clinically meaningful events in healthcare time-series data is crucial for reliable downstream analysis and decision support. However, most existing methods struggle to jointly localize event boundaries and classify event types; even detection transformer (DETR)-based approaches show limited performance when confronted with extremely sparse events typical of clinical recordings. To address these challenges, we propose a coarse-to-fine detection framework combining a global context explorer, a local detail inspector, and an adaptive gating module (AGM) that fuses multiple label perspectives. The AGM uses transformed labels—encoding event presence and temporal position—to improve learning on sparse events.

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. 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. Learning to Defer (L2D) improves AI reliability in decision-critical environments by training AI to either make its own prediction or defer the decision to a human expert. A key challenge is adapting to unseen experts at test time, whose competence can differ from the training population. Current methods for this task, however, can falter when unseen experts are out-of-distribution (OOD) relative to the training population. We identify a core architectural flaw as the cause: they learn identity-conditioned policies by processing class-indexed signals in fixed coordinates, creating shortcuts that violate the problem's inherent permutation symmetry.

ICLR 2026 Poster accepted paper at ICLR 2026. Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness. However, clinical applications demand predictive models that are both accurate and interpretable. We present our Graph Attention-based Relational Learning for Intensive Care (GARLIC) model, a novel neural network architecture that imputes missing data through a learnable exponential-decay encoder, captures inter-sensor dependencies via time-lagged summary graphs, and fuses global patterns with cross-dimensional sequential attention. All attention weights and graph edges are learned end-to-end to serve as built-in observation-, signal-, and edge-level explanations.

ICLR 2026 Oral accepted paper at ICLR 2026. Biosignals offer valuable insights into the physiological states of the human body. Although biosignal modalities differ in functionality, signal fidelity, sensor comfort, and cost, they are often intercorrelated, reflecting the holistic and interconnected nature of human physiology. This opens up the possibility of performing the same tasks using alternative biosignal modalities, thereby improving the accessibility, usability, and adaptability of health monitoring systems. However, the limited availability of large labeled datasets presents challenges for training models tailored to specific tasks and modalities of interest.

ICLR 2026 Poster accepted paper at ICLR 2026. Medical time series (MedTS) classification suffers from poor generalizability in real-world deployment due to inter- and intra-dataset heterogeneity, such as varying numbers of channels, signal lengths, task definitions, and patient characteristics. % implicit patient characteristics, variable channel configurations, time series lengths, and diagnostic tasks. To address this, we propose FORMED, a novel framework for repurposing a backbone foundation model, pre-trained on generic time series, to enable highly generalizable MedTS classification on unseen datasets. FORMED combines the backbone with a novel classifier comprising two components: (1) task-specific channel embeddings and label queries, dynamically sized to match any number of channels and target classes, and (2) a shared decoding attention layer, jointly trained across datasets to capture medical domain knowledge through task-agnostic feature-query interactions.

ICLR 2026 Oral accepted paper at ICLR 2026. Medical question answering (QA) benchmarks often focus on multiple-choice or fact-based tasks, leaving open-ended answers to real patient questions underexplored. This gap is particularly critical in mental health, where patient questions often mix symptoms, treatment concerns, and emotional needs, requiring answers that balance clinical caution with contextual sensitivity. We present CounselBench, a large-scale benchmark developed with 100 mental health professionals to evaluate and stress-test large language models (LLMs) in realistic help-seeking scenarios. The first component, CounselBench-EVAL, contains 2,000 expert evaluations of answers from GPT-4, LLaMA 3, Gemini, and online human therapists on patient questions from the public forum CounselChat.

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. The goal of personalized medicine is to discover a treatment regimen that optimizes a patient's clinical outcome based on their personal genetic and environmental factors. However, candidate treatments cannot be arbitrarily administered to the patient to assess their efficacy; we often instead have access to an *in silico* surrogate model that approximates the true fitness of a proposed treatment. Unfortunately, such surrogate models have been shown to fail to generalize to previously unseen patient-treatment combinations. We hypothesize that domain-specific prior knowledge—such as medical textbooks and biomedical knowledge graphs—can provide a meaningful alternative signal of the fitness of proposed treatments.

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. Federated learning (FL) is increasingly adopted in domains like healthcare, where data privacy is paramount. A fundamental challenge in these systems is statistical heterogeneity—the fact that data distributions vary significantly across clients (e.g., different hospitals may treat distinct patient demographics). While current FL algorithms focus on aggregating model updates from these heterogeneous clients, the potential of the central server remains under-explored. This paper is motivated by a healthcare scenario: could a central server not only coordinate model training but also guide a new patient to the hospital best equipped for their specific condition?

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. LLMs are increasingly being used in healthcare. This promises to free physicians from drudgery, enabling better care to be delivered at scale. But the use of LLMs in this space also brings risks; for example, such models may worsen existing biases. How can we spot when LLMs are (spuriously) relying on patient race to inform predictions? In this work we assess the degree to which Sparse Autoencoders (SAEs) can reveal (and control) associations the model has made between race and stigmatizing concepts. We first identify SAE latents in gemma-2 models which appear to correlate with Black individuals.

ICLR 2026 Oral accepted paper at ICLR 2026. Accurate analysis of Medical time series (MedTS) data, such as Electroencephalography (EEG) and Electrocardiography (ECG), plays a pivotal role in healthcare applications, including the diagnosis of brain and heart diseases. MedTS data typically exhibits two critical patterns: **temporal dependencies** within individual channels and **channel dependencies** across multiple channels. While recent advances in deep learning have leveraged Transformer-based models to effectively capture temporal dependencies, they often struggle to model channel dependencies. This limitation stems from a structural mismatch: ***MedTS signals are inherently centralized, whereas the Transformer's attention is decentralized***, making it less effective at capturing global synchronization and unified waveform patterns. Code/project link: https://github.com/Levi-Ackman/TeCh

ICLR 2026 Poster accepted paper at ICLR 2026. Body composition analysis through CT and MRI imaging provides critical insights for cardio-metabolic health assessment but remains limited by accessibility barriers including radiation exposure, high costs, and infrastructure requirements. We present AbdCTBench, a large-scale dataset containing 23,506 CT-derived abdominal surface meshes from 18,719 patients, paired with 87 comorbidity labels, 31 specific diagnosis codes, and 16 CT-derived biomarkers. Our key insight is that external surface geometry is predictive of internal tissue composition, enabling accessible health screening through consumer devices. We establish comprehensive benchmarks across seven computer vision architectures (ResNet-18/34/50, DenseNet-121, EfficientNet-B0, ViT-Small, Swin Transformer-Base), demonstrating that models can learn robust surface-to-biomarker representations directly from 2D mesh projections. Code/project link: https://abdctbenchrepo.github.io/AbdCTBench/

The PhysioNet/CinC Challenge 2012 dataset contains ICU time-series records used for mortality prediction and patient-specific outcome modeling. It remains a useful benchmark for clinical time-series modeling, missingness-aware learning, and early warning model development.

REFUGE is a retinal fundus imaging challenge dataset for glaucoma assessment. It supports glaucoma classification, optic disc and cup segmentation, fovea localization, and fair comparison of ophthalmology AI methods on color fundus photographs.

MURA is a musculoskeletal radiograph dataset from Stanford for abnormality detection in upper extremity X-rays. It is used for radiology classification, fracture-related screening, musculoskeletal imaging AI, and human-AI comparison studies.

ACDC is a cardiac MRI dataset for automated cardiac diagnosis and segmentation. It supports left and right ventricular segmentation, myocardium segmentation, cardiac function quantification, and evaluation of robust cardiac image analysis methods.

The Cancer Genome Atlas is a large cancer genomics resource with molecular, clinical, and pathology-related data across many cancer types. It is a foundation dataset for oncology AI, survival prediction, subtype discovery, multimodal cancer modeling, and translational biomarker research.

EchoNet-Dynamic is a cardiac ultrasound video dataset with expert annotations for left ventricular function. It is used for echocardiography video understanding, ejection fraction estimation, cardiac segmentation, and clinical video AI research.

CAMELYON17 is a digital pathology dataset for detecting breast cancer metastases in lymph node whole-slide images across multiple centers. It supports pathology classification, metastasis detection, weakly supervised learning, and domain generalization in histopathology AI.

The ISIC Archive is a large public dermatology image repository for skin lesion analysis. It is widely used for melanoma classification, lesion segmentation, dermoscopic image retrieval, bias and domain shift analysis, and clinical imaging benchmark development.

Sleep-EDF Expanded contains polysomnographic sleep recordings with EEG and related physiological signals. It is used for sleep stage classification, biosignal time-series modeling, self-supervised learning on physiological signals, and clinical sleep research benchmarks.

PTB-XL is a large public 12-lead electrocardiography dataset with diagnostic statements and waveform records. It is a standard benchmark for ECG classification, cardiac abnormality detection, clinical signal representation learning, and robust evaluation of biosignal models.

The eICU Collaborative Research Database is a multicenter critical care database containing deidentified ICU data from many hospitals. It is commonly used for external validation, ICU outcome prediction, temporal modeling, and cross-site generalization studies in clinical AI.

MIMIC-IV-ECG is a large deidentified electrocardiogram dataset linked to the MIMIC-IV clinical data ecosystem. It supports ECG classification, arrhythmia detection, representation learning, and multimodal modeling with structured EHR context.

MIMIC-IV is a large deidentified electronic health record dataset from Beth Israel Deaconess Medical Center, covering hospital and ICU data for critical care research. It is a core benchmark source for clinical prediction, temporal EHR modeling, phenotyping, and healthcare AI method development.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. SSL for 3D light-sheet microscopy image segmentation Start date: 2026-06-20. End/deadline date: 2026-09-25.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Multimodal Vessel-Specific Intracranial Aneurysm Classification and Segmentation Challenge Start date: 2026-08-14.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Ischemic Stroke Lesion Segmentation Challenge 2026 Start date: 2026-06-15. End/deadline date: 2026-08-15.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Peripelvic fractures are severe injuries with high disability and mortality rates. The PENGWIN 2026 Challenge aims to advance state-of-the-art techniques for intelligent surgical planning in 3D CT scans. It consists of three tasks: fully automated peripelvic fracture segmentation (Task 1), interactive segmentation (Task 2), and fracture reduction planning (Task 3). The dataset features 500 clinical cases with expert annotations and 16,000 simulated fracture cases to support the training of data-driven reduction models. Start date: 2026-05-10. End/deadline date: 2026-08-19.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. HEad and neCK TumOR Lesion Segmentation, Staging and Prognosis Start date: 2026-05-31. End/deadline date: 2026-07-24.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Automated Lesion Segmentation in Whole-Body PET/CT Start date: 2026-05-03. End/deadline date: 2026-09-25.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Recognition of Anomalies in low-pREvalence cancer Start date: 2026-05-01. End/deadline date: 2026-09-08.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Real-time dose calculation in radiotherapy Start date: 2026-05-31. End/deadline date: 2026-08-31.

Grand Challenge official API lists this medical AI challenge with status OPEN. Lung Nodule Volumetry 2026 Challenge Start date: 2026-03-31. End/deadline date: 2026-06-30.

Grand Challenge official API lists this medical AI challenge with status OPEN. BEETLE is a multicenter, multiscanner benchmark for breast cancer histopathology segmentation. It focuses on multiclass semantic segmentation of hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) into four tissue categories: invasive epithelium, non-invasive epithelium, necrosis, and other. The evaluation set comprises 170 densely annotated regions from 54 WSIs, covering all molecular subtypes and histological grades, thereby capturing much of the morphological heterogeneity seen in clinical practice. BEETLE provides a standardized resource for benchmarking breast cancer segmentation models, supporting the development of robust, generalizable algorithms for large-scale biomarke...

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。

Grand Challenge official API lists this medical AI challenge with status OPEN. Ischemic Stroke Lesion Segmentation Challenge 2024 Start date: 2026-03-30. End/deadline date: 2026-12-31.

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 Gut and Liver collection is open for submissions until 2026-05-12. It welcomes work on risk assessment and early detection of gastrointestinal and liver cancers, including artificial intelligence tools for cancer risk assessment, early-stage detection, novel imaging, tissue acquisition modalities, and health economics for screening.

This Nature Portfolio / npj Genomic Medicine collection is open for submissions until 2026-06-23. It covers AI-powered genomic medicine, including variant prioritization, pathway inference, AI prediction from clinical assays such as histology, radiology and EHRs, multi-omics, precision oncology, rare diseases, population health, explainability, bias, and clinical implementation.

This Nature Portfolio / npj Digital Medicine collection is open for submissions until 2026-06-03. It invites research on real-world clinical performance of AI, including clinical utility, safety, reliability, equity, generalizability, workflow integration, post-deployment monitoring, transparency, patient-centered outcomes, and clinician-AI interaction.

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 2026-07-21. It invites research on AI in sports medicine, including multimodal injury and medical-condition prediction, individualized diagnosis, treatment and rehabilitation, transparent and diverse datasets, open-source explainable AI, and safe AI systems for athlete and exercise health.

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 Nature Portfolio / npj Cardiovascular Health collection is open for submissions until 2026-06-12. It calls for AI and machine learning work in cardiovascular care, including diagnostic evaluation, CVD risk prediction, cardiac imaging and ECG deep learning, EHR tooling, multi-omics, wearables, remote monitoring, deployment, transparency, ethics, and regulation.

This Nature Portfolio collection is open for submissions until 2026-05-15. It focuses on AI technologies for population medicine and public health, including infectious-disease early warning, pathogen detection, chronic disease risk stratification, policy simulation, wearable AI, multimodal fusion, federated learning, privacy preservation, and foundation models.

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 Technologies special issue calls for work on AI-enabled smart healthcare systems. It is relevant to medical AI submissions on intelligent monitoring, anomaly detection, assistive technologies, smart sensing, clinical decision support, and AI-assisted healthcare workflows. The page lists a manuscript submission deadline of 2026-08-30.

This Healthcare special issue focuses on artificial intelligence in public health, healthcare services, and management, including disease surveillance, outbreak prediction, GenAI in diagnostics, imaging and EHRs, patient engagement, and AI ethics and equity. The MDPI flyer lists a manuscript submission deadline of 2026-07-31.

This Diagnostics special issue calls for work on artificial intelligence for health and medicine, in a journal section focused on machine learning and AI in diagnostics. It is relevant to medical imaging, diagnostic pathology and radiology, digital health, rehabilitation, cybersecurity, patient safety, and clinical AI quality. The page lists a manuscript submission deadline of 2026-12-31.

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.

This Applied Sciences special issue calls for AI-driven healthcare work, including explainable and trustworthy AI, EHR and clinical text, bias and fairness, public health and epidemiology, translational AI, real-world clinical deployment, benchmarks, and reproducibility. The page lists a manuscript submission deadline of 2026-08-30.

The INFORMS Journal on Data Science CFP focuses on artificial intelligence and data science for healthcare. It is relevant to medical AI work on clinical prediction, decision support, operations, fairness, reliability, and deployment of data-driven healthcare systems. The CFP page lists a 2026 special issue call and submission timeline.

This Frontiers Research Topic calls for work on integrating multi-omics data with clinical information to improve diagnosis, prognosis, and personalized treatment. The page lists a manuscript deadline of 2026-09-14 and is currently accepting articles, making it a relevant journal CFP for clinical translation, multimodal medical AI, and precision medicine.

This Taylor & Francis / Risk Management and Healthcare Policy article collection focuses on artificial intelligence in healthcare risk management, including AI applications for early disease detection, personalized treatment optimization, real-time risk prediction, patient safety, and health-system risk management. The CFP listing gives a 2026-05-31 submission deadline.

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 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 ICDM 2026 with full papers due 2026-06-06 AoE, after an abstract deadline on 2026-05-30, and conference dates 2026-11-12 to 2026-11-15 in Shenyang. ICDM is relevant to clinical data mining, EHR prediction, risk stratification, biomedical temporal modeling, and trustworthy health analytics.

CCF-Deadlines lists APBC 2026 with papers due 2026-06-15 UTC+0 and conference dates 2026-10-21 to 2026-10-24 in Hsinchu. APBC is directly relevant to medical AI through bioinformatics, computational biology, multi-omics modeling, precision medicine, and AI-assisted biomedical discovery.

Stanford MED277 / CS337 AI-Assisted Health Care examines how to move technical advances into clinical settings, choose the right healthcare problems, and improve outcomes that matter. It is geared toward learners who want to use AI and machine learning for real-world human health impact.

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.

MIT OCW 6.S897 Machine Learning for Healthcare introduces clinical data and machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, medical imaging, public health, and clinical workflow improvement.

MIT OCW HST.953 focuses on practical considerations for operationalizing machine learning in healthcare settings. It is relevant for learners moving from clinical data modeling into visualization, deployment, workflow, and real-world healthcare AI implementation.

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.

FutureLearn's Artificial Intelligence for Healthcare course covers AI concepts, tools, ethics, diagnostics, patient care, operational efficiency, personalized medicine, and robotic surgery. The page lists 8 weeks at 2 hours per week and introductory level.

This University of Manchester and Health Education England FutureLearn course introduces AI for healthcare professionals, including how AI could support the workforce, real-world examples in radiology, pathology, nursing, ethical issues, logistics, and financial challenges. The page lists 5 weeks at 2 hours per week.

This University of Colorado System course introduces how AI shapes modern health systems, including key applications, adoption trends, digital transformation, health equity, and opportunities for addressing grand healthcare challenges. Coursera lists it as beginner level and 8 hours.

The Broad Institute ML4H Clinical AI Seminar Series features talks from leading experts at the intersection of AI and medicine. Topics include generative and foundation models, ethical and responsible AI, self-supervised learning, medical imaging, digital twins, and real-world clinical applications.