搜索医学 AI 论文与资源

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. 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.

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.

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.