SurvAgent: Hierarchical CoT-Enhanced Case Banking and Dichotomy-Based Multi-Agent System for Multimodal Survival Prediction

Transparency in high-stakes medical decisions: SurvAgent addresses a critical gap in clinical AI adoption by providing explainable reasoning for survival predictions in cancer treatment. The system documents its complete analytical process through "chain-of-thought" reasoning, allowing clinicians to understand why the AI made specific predictions rather than treating it as a "black box"—a key requirement for responsible deployment in healthcare settings.

Learning from past cases improves predictions: The system uses a "case bank" that stores detailed analyses of historical patient cases, allowing it to retrieve and learn from similar previous cases when making new predictions. This approach mirrors how experienced oncologists draw on their clinical experience, potentially making AI recommendations more grounded in real-world patient outcomes rather than purely statistical patterns.

Combining multiple data types for better accuracy: SurvAgent integrates pathology images (tissue slides), genetic data, and other patient information simultaneously. Testing across five cancer datasets showed this multimodal approach outperformed both conventional methods and existing AI systems, suggesting that responsible AI for cancer prognosis should synthesize diverse medical evidence rather than relying on single data sources.

Structured reasoning enables clinical validation: By breaking down predictions into discrete analytical steps (screening tissue regions, analyzing gene categories, refining predictions progressively), the system creates checkpoints where clinicians can verify each stage of reasoning. This structured approach supports the responsible AI principle that high-stakes decisions should be auditable and contestable by domain experts.