Multi-Agent Reasoning with Consistency Verification Improves Uncertainty Calibration in Medical MCQA
arXiv:2603.24481v2 Announce Type: replace-cross Abstract: Miscalibrated confidence scores are a practical obstacle to deploying AI in clinical settings. A model that is always overconfident offers no useful signal for deferral. We present a multi-agent framework that combines domain-specific specialist agents with Two-Phase Verification (Wu et al., 2024) and S-Score Weighted Fusion to improve both calibration and discrimination in medical multiple-choice question answering. Four specialist agents (respiratory, cardiology, neurology, gastroenterology) generate independent diagnoses using Qwen2.
The deployment of AI in sensitive applications like medicine necessitates robust uncertainty calibration to ensure safety and trustworthiness, a key focus as AI models become more capable.
Improving AI model confidence calibration and discrimination in medical diagnostics directly addresses a critical barrier to clinical adoption, potentially saving lives and improving patient outcomes.
The proposed multi-agent framework with consistency verification offers a new pathway to making medical AI more reliable and deployable, shifting from overconfident, miscalibrated systems to more trustworthy diagnostic aids.
- · AI developers in healthcare
- · Healthcare providers
- · Patients
- · Medical AI companies
- · AI models without calibration mechanisms
- · Healthcare systems hesitant to adopt AI despite advancements
More accurate and reliable AI diagnoses in specific medical fields.
Increased trust and accelerated adoption ofAI diagnostic tools in clinical settings.
AI becomes a standard, indispensable part of medical decision-making, leading to better public health outcomes and reduced diagnostic errors.
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Read at arXiv cs.LG