向公平的医疗咨询:解决和减轻基于大型语言模型的医疗保健应用中的偏见
Haohui Lu1, Ye Lin2, Zhidong Li2
1Molly Wardaguga Institute for First Nations Birth Rights, Faculty of Health, Charles Darwin University, Brisbane, Queensland, Australia.
Artificial intelligence in medicine
|July 18, 2025
概括
公平医疗减少了人工智能医疗建议中的人口偏见,提高了医疗保健应用中的公平和公平. 这一框架改善了大型语言模型 (LLM) 对不同人群的输出.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 健康 公平 卫生 公平
背景情况:
- 大型语言模型 (LLM) 越来越多地用于基于网络的医疗咨询系统.
- 法律学士学位的成果可能表现出人口偏见,影响弱势群体的公平性.
研究的目的:
- 引入FairMed,一个框架来缓解LLM产生的医疗咨询中的人口偏见.
- 通过使用各种指标和数据集来评估FairMed的有效性.
主要方法:
- 在FairMed框架内采用了微调和快速工程策略.
- 评估涉及跨人口群体的基于语言和内容水平的指标.
- 数据集包括公开可用 (MedQA),合成 (Synthea) 和私人 (CBHS) 数据.
主要成果:
- 与基线模型和零射击提示相比,FairMed表现出了持续的改进.
- 在性别和种族群体中,统计学平价差异 (SPD) 和不平衡影响比率的显著减少被观察到.
- 诊断推分歧 (DRD),行动差异指数 (ADI) 和推频率平价 (RFP) 等指标的改善也得到了注意.
结论:
- 公平医疗有效地解决了人工智能驱动的医疗咨询中的人口差异.
- 该框架促进公平的医疗保健指导,并建立可信,包容的系统.
- 这项工作有助于在医疗保健中更公平,更敏感地应用LLM.
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