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arXiv AI By Benjamin C Liu, Dillon Mehta, Rishi Malhotra, Adam Zobian, Yong Ying Tan, Samir Chopra, Daniella Rand, Natalie Pang, Abhiram Gudimella, Kevin Zhu

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

Read the original on arXiv AI →

The paper investigates how human interventions at specific fault points—moments when an AI agent’s reasoning is most vulnerable—affect the diagnostic accuracy of multi‑agent medical systems. Using the MedQA dataset, the authors found that correct interventions can boost baseline accuracy by up to 40%, whereas incorrect or bias‑related interventions can reduce performance by up to 6% and increase diagnostic drift and uncertainty. The study also highlights behavioral parallels between cognitive biases observed in simulated agent conversations and real‑world clinical practice, such as premature closure and susceptibility to misleading cues.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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