AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks

ArXi:2605.10286v1 Announce Type: new Building effective clinical decision systems requires the synthesis of complex heterogeneous multimodal data. Such modalities include temporal electronic health records data, medical images, radiology reports, and clinical notes. Large language model (LLM)-based agents have shown impressive performance in various healthcare tasks, especially those involving textual modalities. Considering the fragmentation of healthcare data across hospital systems, collaborative agent frameworks present a promising direction to mitigate data sharing challenges.