OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction
arXiv:2510.17532v2 Announce Type: replace Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data. While large language models (LLMs) have shown strong performance in biomedical NLP, they often lack structured reasoning capabilities critical for high-stakes decision support. We present a unified, multi-task learning framework that aligns autoregressive LLMs with clinical reasoning for outcome prediction on the MSK-CHORD dataset. Our models are trained to jointly perform binary survival
The increasing performance of LLMs combined with the critical need for explainability in high-stakes medical applications motivates this research in structured clinical reasoning.
This development addresses a key limitation of current AI in healthcare by integrating robust, interpretable reasoning into LLMs for survival prediction, enhancing trust and clinical utility.
AI models can now provide more transparent and justifiable insights for medical prognostics, potentially accelerating AI adoption in oncology and similar fields.
- · Healthcare AI developers
- · Oncology patients
- · Medical researchers
- · Hospitals and clinics
- · Black box AI solutions lacking interpretability
- · Companies relying solely on traditional statistical models
Improved accuracy and adoption of AI in cancer treatment planning due to enhanced interpretability.
Expansion of similar structured reasoning frameworks to other complex medical diagnoses and treatment predictions.
Increased regulatory clarity and public acceptance for autonomous AI systems in sensitive healthcare domains, potentially leading to faster integration into clinical practice.
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Read at arXiv cs.CL