With the growing utilization of machine learning in healthcare, there is increasing potential to enhance healthcare outcomes and efficiency. However, this also brings the risk of perpetuating biases in data and model design that can harm certain protected groups based on factors such as age, gender, and race. This study proposes an artificial intelligence framework, grounded in software engineering principles, for identifying and mitigating biases in data and models while ensuring fairness in healthcare settings. A case study is presented to demonstrate how systematic biases in data can lead to amplified biases in model predictions, and machine learning methods are suggested to prevent such biases. Future research aims to test and validate the proposed ML framework in real-world clinical settings to evaluate its impact on promoting health equity.

本研究提出了一种基于软件工程原理的人工智能框架，用于在保证医疗保健公平的同时识别和缓解数据和模型中的偏见和歧视，通过案例研究表明数据中的系统性偏见会导致模型预测中的偏差增大，并提出了机器学习方法来预防这种偏见。未来的研究目标是在实际的临床环境中测试和验证所提出的ML框架，以评估其在促进健康公平方面的影响。

机器学习中的公平性与医疗保健中的公平性相遇