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In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus on different methods used for creating synthetic data. This paper explores several mitigating algorithms like SMOTE, AdaSyn, Fair-SMOTE, and BayesBoost. The optimized Bayesboost algorithm has been discussed. This approach showed more accuracy and addressed the error handling mechanism.
Parate et al. (Fri,) studied this question.