Machine Learning for Personalized Medicine: Transforming Patient Care with Artificial Intelligence

Abstract

Machine learning revolutionizes personalized medicine by facilitating personalized healthcare based on data. This review goes through its contribution to the shift from conventional therapies to precision medicine, by incorporating genomic, clinical, imaging and wearable data. Supervised learning, Unsupervised learning, deep learning - all of these machine learning approaches are applicable to some of the medical uses of machine learning such as disease prediction, treatment optimization, drug discovery, and clinical decision support. While there has been considerable progress, there are still issues to be addressed, including data privacy, bias, interpretability, and regulatory compliance. Ethical and legal issues also are important to implementation. The future of health care delivery is anticipated to become even more accurate and individualized, with digital twins, federated learning, and real-time AI systems all on the horizon.