  With the growing utilization of machine learning in healthcare, there is
increasing potential to enhance healthcare outcomes. However, this also brings
the risk of perpetuating biases in data and model design that can harm certain
demographic 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.
