搜索医学 AI 论文与资源

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 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 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. The growing demand for prenatal ultrasound imaging has intensified a global shortage of trained sonographers, creating barriers to essential fetal health monitoring. Deep learning has the potential to enhance sonographers' efficiency and support the training of new practitioners. Vision-Language Models (VLMs) are particularly promising for ultrasound interpretation, as they can jointly process images and text to perform multiple clinical tasks within a single framework. However, despite the expansion of VLMs, no standardized benchmark exists to evaluate their performance in fetal ultrasound imaging. Code/project link: https://github.com/BioMedIA-MBZUAI/FETAL-GAUGE

ICLR 2026 Poster accepted paper at ICLR 2026. Current medical language model (LM) benchmarks often over-simplify the complexities of day-to-day clinical practice tasks and instead rely on evaluating LMs on multiple-choice board exam questions. In psychiatry especially, these challenges are worsened by fairness and bias issues, since models can be swayed by patient demographics even when those factors should not influence clinical decisions. Thus, we present an expert-created and annotated dataset spanning five critical domains of decision-making in mental healthcare: treatment, diagnosis, documentation, monitoring, and triage. This U.S. centric dataset — created without any LM assistance — is designed to capture the nuanced clinical reasoning and daily ambiguities mental health practitioners encounter, reflecting the inherent complexities of care delivery that are missing from existing datasets.

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 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 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 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.