Examining the Vulnerability of Multi-Agent Medical Systems to Human Interventions for Clinical Reasoning

2026-09-02

AI Summary

研究通过MedQA数据集分析了人类干预对多智能体医疗系统诊断准确性的影响，正确干预可提高准确性，错误干预则降低性能。

Abstract

Human interventions at fault points can alter the diagnostic accuracy of multi-agent medical systems. We defined fault points as moments in AI agent conversations, in which an agent's reasoning became most vulnerable to external influence. Using the MedQA dataset, this study analyzed simulated doctor-patient conversations to measure how interventions shifted reasoning and accuracy. Correct intervention methods showed an improvement in baseline diagnostic accuracy of up to 40%, while incorrect or bias-related interventions degraded performance by up to 6% and increased diagnostic drift and uncertainty. Beyond performance changes, our analysis revealed behavioral similarities between cognitive biases in simulated agent environments and real-world clinical practice. Examples included premature closure and susceptibility to misleading cues. Overall, these findings demonstrate that identifying and guiding fault points with human interventions may provide a mechanism for improving diagnostic robustness in multi-agent medical systems.

Problem

• Multi-Agent Medical Systems
• Human Interventions
• Clinical Reasoning
• Diagnostic Accuracy
• Fault Points

Innovation

• fault points
• human interventions
• diagnostic accuracy
• cognitive biases
• multi-agent medical systems