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Agentic LLM Systems Show Promise but Remain Unreliable for Breast Cancer Treatment Recommendations

Jul 15, 2026

Researchers evaluated agentic large language model (LLM) systems for generating breast cancer treatment recommendations using 72 real clinical cases and 1,147 case-specific rubrics. The best-performing system, Claude Opus 4.8 with a D&C+SA pipeline, achieved a global score of 0.594, but oncologist-led error analysis found persistent clinically relevant failures, including incorrect or missing recommendations, flawed justifications, citation errors, outdated claims, and overconfidence.

Why it matters: The study demonstrates that while agentic LLMs can generate clinically relevant recommendations, they are not yet reliable enough for unsupervised clinical use in oncology.

Full story at: arXiv Computation and Language ↗

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