# Agentic systems for breast cancer treatment recommendations

arXiv: https://arxiv.org/abs/2607.12051 · HTML: https://mlsanity.com/paper/2607.12051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 16 of 16)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models](https://mlsanity.com/paper/2310.08491/) (2023)
- [HealthBench: Evaluating Large Language Models Towards Improved Human Health](https://mlsanity.com/paper/2505.08775/) (2025)
- [Measuring AI Ability to Complete Long Software Tasks](https://mlsanity.com/paper/2503.14499/) (2025)
- [Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine](https://mlsanity.com/paper/2401.08396/) (2024)
- [HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats](https://mlsanity.com/paper/2604.27470/) (2026)
- [SedarEval: Automated Evaluation using Self-Adaptive Rubrics](https://mlsanity.com/paper/2501.15595/) (2025)
- [Large language models provide unsafe answers to patient-posed medical questions](https://mlsanity.com/paper/2507.18905/) (2025)
- [Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models](https://mlsanity.com/paper/2606.07157/) (2026)
- [RubricRAG: Towards Interpretable and Reliable LLM Evaluation via Domain Knowledge Retrieval for Rubric Generation](https://mlsanity.com/paper/2603.20882/) (2026)
- [Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses](https://mlsanity.com/paper/2509.16093/) (2025)
- [Generating and Refining Dynamic Evaluation Rubrics for LLM-as-a-Judge](https://mlsanity.com/paper/2605.30568/) (2026)
- [Expert Evaluation of Clinical AI Tools on Real Point-of-Care Clinical Queries](https://mlsanity.com/paper/2606.28960/) (2026)
- [Retrieval-Augmented Agentic Rubric Generation for Reliable Medical Response Evaluation](https://mlsanity.com/paper/2601.15161/) (2026)
- [RiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction](https://mlsanity.com/paper/2503.03802/) (2025)
- [Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs](https://mlsanity.com/paper/2605.30646/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly being explored for clinical decision support, but their reliability in complex oncology treatment planning remains unclear. We evaluated agentic LLM systems for breast cancer treatment recommendation generation using 72 real clinical cases across stages I to IV and 1,147 case-specific rubrics generated through Asymmetric Information Rubric Generation (AIRG), in which the rubric generator had access to real clinical decisions unavailable to the evaluated models. Seven pipelines were compared, including single-LLM baselines, tool-augmented systems, and multi-agent architectures with fact checking and autonomous subagent spawning. The best-performing configuration, Claude Opus 4.8 with the D&C+SA pipeline, achieved a global score of 0.594 ± 0.025. Tool use and increased agent autonomy had mixed effects, improving performance in some settings but degrading it in others. Performance varied by clinical domain and disease stage, and oncologist-led error analysis revealed persistent clinically relevant failures, including incorrect or missing recommendations, flawed justifications, citation errors, outdated claims, and overconfidence. These findings suggest that agentic LLM systems can generate clinically relevant breast cancer recommendations, but remain insufficient for unsupervised clinical use.