Biased AI-generated medical advice and misdiagnoses can jeopardize patient safety, making the integrity of AI in healthcare more critical than ever. As Large Language Models (LLMs) take on a growing role in medical decision-making, addressing their biases and enhancing their accuracy is key to delivering safe, reliable care. This study addresses these challenges head-on by introducing new resources designed to promote ethical and precise AI in healthcare. We present two datasets: BiasMD, featuring 6,007 question-answer pairs crafted to evaluate and mitigate biases in health-related LLM outputs, and DiseaseMatcher, with 32,000 clinical question-answer pairs spanning 700 diseases, aimed at assessing symptom-based diagnostic accuracy. Using these datasets, we developed the EthiClinician, a fine-tuned model built on the ChatDoctor framework, which outperforms GPT-4 in both ethical reasoning and clinical judgment. By exposing and correcting hidden biases in existing models for healthcare, our work sets a new benchmark for safer, more reliable patient outcomes.

此研究解决了大型语言模型在医疗领域中存在的偏见和不准确性问题。通过引入BiasMD和DiseaseMatcher两个数据集，并开发出EthiClinician模型，该模型在伦理推理和临床判断方面超越了GPT-4。研究的成果能够提高AI在医疗中的安全性与可靠性，改善患者的诊断结果。

检测偏见与提高大型语言模型在医疗中的诊断准确性