SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction
SAGEAgent is a self-evolving agent for cost-aware modality acquisition in multimodal survival prediction, as described in a paper on arXiv cs.AI published on 2026-07-10.
SAGEAgent is a self-evolving agent designed to reduce diagnostic burden by selectively acquiring modalities for multimodal survival prediction in oncology, following a clinically mandated order of escalating burden.
The agent uses a self-evolving mechanism to balance cost and accuracy, suggesting a reinforcement learning or meta-learning approach for modality selection.
This work addresses the practical challenge of unnecessary diagnostic procedures in oncology, potentially reducing healthcare costs and patient burden.
If validated, SAGEAgent could be commercialized as a clinical decision support tool, reducing unnecessary tests and associated costs.
Future work may involve clinical validation and integration with hospital systems; next signal: publication of follow-up studies with real-world patient data.