Abstract
This review paper delves into the transformative potential and challenges of Machine Learning (ML) in the field of biomedical diagnostics and disease prediction. With the advent of advanced computational models and an ever-increasing availability of biomedical data, ML has the potential to revolutionize diagnostic methodologies, enhance predictive accuracy, and streamline therapeutic interventions. However, the application of these technologies is not without its challenges, including issues of data quality, algorithmic bias, and ethical concerns, which must be addressed to leverage ML’s full potential effectively.
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