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The advent of machine learning in personalized medicine has revolutionized the healthcare industry by providing an enhanced diagnosis and treatment regimen to patients based on their unique characteristics such as genetic predispositions, lifestyle variables, and medical history. Machine learning algorithms can analyze vast amounts of patient data to generate accurate diagnoses, establish tailored treatment plans, and improve patient outcomes. By combining multiple data sources, machine learning algorithms can identify patterns, predict the likelihood of specific illnesses, and recommend personalized treatment options. The technology has enabled healthcare professionals to access diverse datasets, including genetic information, medical history, and lifestyle variables, and derive insights from them that were previously inaccessible. However, the isolation of user data in silos across multiple hospitals and medical institutions presents challenges for researchers working in this sector. The article examines the possible obstacles and ethical implications associated with the widespread implementation of machine learning in personalized medicine and assesses the consequences of these breakthroughs for patient care, healthcare systems, and the future of medical research.
Kumar et al. (Wed,) studied this question.