# Ensuring Ground Truth Accuracy in Healthcare with the EVINCE framework

arXiv: https://arxiv.org/abs/2405.15808 · HTML: https://mlsanity.com/paper/2405.15808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Prompting Large Language Models With the Socratic Method](https://mlsanity.com/paper/2303.08769/) (2023)
- [Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra](https://mlsanity.com/paper/2404.03647/) (2024)
- [Machine learning based disease diagnosis: A comprehensive review](https://mlsanity.com/paper/2112.15538/) (2021)
- [Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models](https://mlsanity.com/paper/2405.07076/) (2024)

## Abstract (arXiv)

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.