Bias detection and mitigation in AI models trained on clinical datasets

The usage of Artificial Intelligence (AI) models has penetrated clinical decision-making systems, being used in diagnostics as well as recommendation of treatment. Nevertheless, these models may be poor because of occurring biases in the clinical datasets which are utilized in training. Such biases are likely to lead to unbalanced performance in various demographics, which is ethically, legally, and clinically problematic. This paper examines origin and source of bias in clinical AI models and methods of detection as well as executing mitigation measures such as reweighting, data augmentation, and algorithms fairness measures. Evidence-based on experimental analysis using benchmark clinical datasets illustrates how the over-looked bias may produce the unequal effects on the gender, age, and ethnicity subgroups. Model fairness scores went up without a drastic accuracy sacrifice following the implementation of mitigation strategies. These findings raise the need to produce equitable and credible applications with the help of bias-aware AI development pipelines in healthcare environments.