AI and Machine Learning in Healthcare: Advancing Diagnostics, Personalized Treatment, and Predictive Modeling

Background: Machine learning (ML) has profoundly revolutionized the health- care sector by enhancing diagnostic precision, forecasting patient outcomes, individual- izing treatment strategies, and streamlining healthcare processes. Notwithstanding its progress, issues of data privacy, security, algorithmic bias, and model openness impede extensive implementation. Methods: This review consolidates current research on machine learning applica- tions in healthcare, emphasizing supervised, unsupervised, and reinforcement learning methodologies. It examines their functions in illness diagnosis, risk evaluation, med- ical image analysis, and tailored therapy approaches. This research also investigates new technologies, such as federated learning and hybrid models, designed to tackle data-related difficulties while safeguarding patient privacy. Results: Supervised learning has greatly enhanced clinical decision-making, espe- cially in illness identification and patient surveillance. Deep learning, particularly convolutional neural networks (CNNs), has transformed medical image processing, enhancing the early identification of illnesses like skin cancer and diabetic retinopa- thy. Reinforcement learning has shown potential in robotic surgery and individualized treatment planning. Nonetheless, obstacles such as disjointed healthcare data, ethical dilemmas, and legal limitations persist in affecting the deployment of machine learning. Conclusion: The future of machine learning in healthcare depends on advancing model interpretability, augmenting data sharing frameworks, and incorporating ethical concerns to guarantee equitable and dependable healthcare provision. Progress in privacy-preserving methodologies and multidisciplinary partnerships will be essential in addressing current obstacles and promoting the ethical implementation of machine learning in clinical practice and policy formulation.