Bias Mitigation in AI Models for Cardiovascular Diseases Prediction

Bias in artificial intelligence algorithms used in healthcare is a growing concern due to its potential to negatively impact patient outcomes. AI tools, despite their power, can perpetuate systematic unfairness towards certain groups. This is particularly critical in healthcare, where existing biases can exacerbate inequalities. Historical examples, such as the Fram- ingham Risk Score, demonstrate how biases in traditional medical tools have led to variable accuracy across different ethnic groups and genders, due to the underrepresentation of diverse cohorts in their development. Women, for instance, experience higher rates of missed and delayed car- diovascular disease diagnoses, partly because of their underrepresentation in clinical trials. While some level of bias in data and tools is inevitable, the validation of AI tools to minimize bias offers a promising opportunity to reduce disparities in healthcare outcomes.