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cs.AI2026

EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild

Yuyang Dai, Zheng Chen, Jathurshan Pradeepkumar +4

Epilepsy diagnosis and treatment require evidence-intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mechanisms, pharmacogenomics, t…

cs.AI2026

How Well Do Multimodal Models Reason on ECG Signals?

Maxwell A. Xu, Harish Haresamudram, Catherine W. Liu +11

While multimodal large language models offer a promising solution to the "black box" nature of health AI by generating interpretable reasoning traces, verifying the validity of the…

cs.AI2026

ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks

Haohui Jia, Zheng Chen, Lingwei Zhu +6

Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model con…

cs.AI2025

Developing Large Language Models for Clinical Research Using One Million Clinical Trials

Zifeng Wang, Jiacheng Lin, Qiao Jin +5

Developing artificial intelligence (AI) for clinical research requires a comprehensive data foundation that supports model training and rigorous evaluation. Here, we introduce Tria…

cs.AI2025

Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development

Chufan Gao, Jathurshan Pradeepkumar, Trisha Das +2

Background The cost of drug discovery and development is substantial, with clinical trial outcomes playing a critical role in regulatory approval and patient care. However, access…