搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. We study logical reasoning in language models by asking whether their errors follow established human fallacy patterns. Using the Erotetic Theory of Reasoning (ETR) and its open‑source implementation, PyETR, we programmatically generate 383 formally specified reasoning problems and evaluate 38 models. For each response, we judge logical correctness and, when incorrect, whether it matches an ETR‑predicted fallacy. Two results stand out: (i) as a capability proxy (Chatbot Arena Elo) increases, a larger share of a model’s incorrect answers are ETR‑predicted fallacies ($\rho=0.360, p=0.0265$), while overall correctness on this dataset shows no correlation with capability; (ii) reversing premise order significantly reduces fallacy production for many models, mirroring human order effects.

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.

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.

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 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.