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This paper examines the integration of multi-modal clinical data, including treatment records, pathology reports, and clinician notes, using rule-based extraction and machine learning to improve breast cancer recurrence prediction compared to single-modal approaches.
This paper presents a deployment-oriented stress-testing framework to evaluate how well large language models identify side effects of breast cancer radiation treatments. The study highlights limitations in LLM reliability, such as sensitivity to minor documentation changes and under-recall of rare side effects, suggesting that grounding outputs in clinician-curated lists improves robustness.