OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction
Researchers have developed a new framework called OncoReason to improve the interpretability and accuracy of large language models (LLMs) in predicting cancer treatment outcomes. This multi-task learning approach trains LLMs to perform survival classification, time regression, and generate natural language rationales for their predictions. Experiments using LLaMa3-8B and Med42-8B models showed that Chain-of-Thought prompting and Group Relative Policy Optimization significantly enhanced predictive performance and interpretability, setting a new benchmark for trustworthy LLMs in oncology. AI
IMPACT Enhances LLM interpretability and accuracy for clinical decision support, potentially improving patient outcomes.