LLM Agents for Multimodal Clinical Prediction: A Benchmark Study
A new study from arXiv systematically evaluates LLM-based agents for multimodal clinical prediction tasks using large-scale real-world data. The research assesses performance in unimodal and multimodal settings, comparing single-agent and multi-agent systems. It highlights the potential of collaborative agent frameworks to address data sharing challenges in fragmented healthcare systems, though effectiveness for multimodal risk prediction remains largely unexamined.
Key facts
- The study is published on arXiv with ID 2605.10286.
- It evaluates LLM agents for clinical prediction tasks.
- Data includes temporal EHR, medical images, radiology reports, and clinical notes.
- Performance gaps between single-agent and multi-agent systems are quantified.
- Collaborative agent frameworks may mitigate data sharing challenges.
- Effectiveness for multimodal clinical risk prediction is largely unexamined.
Entities
Institutions
- arXiv