Ensuring Ground Truth Accuracy in Healthcare with the EVINCE framework
This addresses misdiagnosis issues for patients, but appears incremental as it builds on existing LLM methods with a new framework.
The paper tackles the problem of misdiagnosis in healthcare by proposing the EVINCE framework, which uses a novel theory and multiple LLMs in a structured debate to improve diagnosis accuracy and rectify errors, with empirical verification of its effectiveness.
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.