arXiv:2510.10185v2 Announce Type: replace Abstract: Large language models are increasingly being assembled into medical multi-agent systems that emulate multidisciplinary consultation through specialist roles, peer review and consensus formation. In clinical decision support, however, apparent consensus is not enough. Clinicians also need to know whether agents checked the evidence, addressed disagreement and kept uncertainty visible. Current evaluations largely score final accuracy, leaving the safety of the collaborative process untested. Here we introduce MedAgentAudit, a clinically grounde
The rapid deployment of multi-agent AI systems in critical fields like medicine necessitates immediate rigorous evaluation beyond simple accuracy metrics.
This highlights the inherent risks in complex AI deployments where consensus can mask fundamental errors or ignored evidence, impacting patient safety and trust in AI systems.
The focus for evaluating multi-agent AI shifts from solely outcome accuracy to include auditability of the internal collaborative and decision-making processes.
- · AI audit and safety companies
- · Clinical ethics boards
- · Healthcare regulatory bodies
- · Developers of transparent AI systems
- · AI developers prioritizing speed over safety
- · Black-box multi-agent AI systems
- · Healthcare providers relying solely on AI consensus
- · Patients harmed by uncritical AI deployment
Increased demand for explainable AI and auditable multi-agent architectures in sensitive sectors.
New regulatory frameworks and compliance standards for AI agentic systems are expedited, potentially slowing initial deployment but increasing long-term trustworthiness.
Public distrust in AI grows if highly publicized failures occur due to unchecked 'false consensus', leading to a broader societal debate on AI autonomy and accountability.
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