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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.
A new study proposes viewing tumors as organized ecosystems rather than random mutations, using AI to analyze spatial organization, immune localization, and signaling environments in oncology.
FD-NL2SQL is a feedback-driven natural language to SQL system for clinical oncology databases that improves with use through clinician edits and logic-based SQL augmentation. The system decomposes natural language questions into predicates, retrieves expert-verified exemplars, and synthesizes executable SQL with continuous learning capabilities.