核心要点
- 问题/背景
- This npj Digital Medicine paper evaluates agentic LLM systems in clinical decision workflows rather than treating medical AI as isolated question answering. It compares open-source and proprietary planner-executor-verifi...
- 方法/机制
- The main result is cautionary: tool-using clinical agents deliver only modest accuracy gains while increasing token use and latency substantially. Built-in safeguards filter many hallucinations but do not eliminate them.
- 结果/证据
- For this repository, the paper is useful because it supplies a concrete evaluation pattern for agent systems in a high-stakes workflow: accuracy, tool cost, latency, multimodal failure, hallucination filtering, and clini...
- 收录价值
- It is not collected because the systems themselves are strong; it is collected because the benchmark and negative result are reusable for medical operations and agent evaluation.
收录解读
This npj Digital Medicine paper evaluates agentic LLM systems in clinical decision workflows rather than treating medical AI as isolated question answering. It compares open-source and proprietary planner-executor-verifier style agents across diagnostic simulation, medical QA, and hard multimodal/text benchmarks.
The main result is cautionary: tool-using clinical agents deliver only modest accuracy gains while increasing token use and latency substantially. Built-in safeguards filter many hallucinations but do not eliminate them.
For this repository, the paper is useful because it supplies a concrete evaluation pattern for agent systems in a high-stakes workflow: accuracy, tool cost, latency, multimodal failure, hallucination filtering, and clinical viability must be measured together.
It is not collected because the systems themselves are strong; it is collected because the benchmark and negative result are reusable for medical operations and agent evaluation.
论文摘要
The paper evaluates LLM-based agent systems for clinical decision tasks across AgentClinic, MedAgentsBench, and Humanity's Last Exam, finding modest gains over baseline LLMs but high token and latency costs and persistent hallucination issues.