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The results identify a substantial domain gap between veterinary and human pathology, expose the risk of over-diagnosis of normal tissue in frontier models, and show that domain-specific training remains critical for visually grounded predictions.
Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similar...
It is shown that Monte Carlo dropout, applied to a multi-task chest-radiograph classifier, provides an epistemic uncertainty signal that tracks generalisation across training-set scales and flags confident yet error-prone predictions.
It is demonstrated that pairing a genomic language model with widely available clinical imaging can serve as a hypothesis-free discovery tool for gene--imaging associations invisible to conventional approaches.
These findings show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-dependent safety risk, and show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-dependent safety risk.
Abstract Background Maintenance of oncology clinical practice guidelines (CPGs) is increasingly challenged by the rapid growth of trial data and therapeutic complexity. While large language models (LLMs) have shown promise in information retrieval, their utility in the rigorous, end-to-end workflow of guideline mainten...
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