  Misdiagnosis is a significant issue in healthcare, leading to harmful
consequences for patients. The propagation of mislabeled data through machine
learning models into clinical practice is unacceptable. This paper proposes
EVINCE, a system designed to 1) improve diagnosis accuracy and 2) rectify
misdiagnoses and minimize training data errors. EVINCE stands for Entropy
Variation through Information Duality with Equal Competence, leveraging this
novel theory to optimize the diagnostic process using multiple Large Language
Models (LLMs) in a structured debate framework. Our empirical study verifies
EVINCE to be effective in achieving its design goals.
