Foundations of Machine Learning and Its Applications in Healthcare: A Review of Methods, Clinical Impact, and Open Challenges

Healthcare systems generate large volumes of imaging, laboratory, genomic, medication, sensor, and narrative clinical data. Machine learning provides methods for learning patterns from these data and converting them into predictions that may support diagnosis, prognosis, workflow prioritization, and biomedical discovery. This narrative review explains the principles of machine learning for healthcare readers and connects them to major clinical applications. It begins with an introduction to supervised, unsupervised, semi-supervised, and reinforcement learning, followed by common algorithms, deep-learning architectures, model training, generalization, evaluation metrics, and calibration. The review then examines medical imaging, electronic health records analytics, risk prediction, decision support, genomics, drug discovery, drug-drug interaction prediction, and clinical natural language processing. Evidence from landmark studies is discussed alongside recent guidance on external validation, reporting, foundation models, federated learning, explainability, and implementation. The review concludes that machine learning has achieved clinically meaningful performance in selected tasks, but safe adoption requires prospective testing, fairness assessment, privacy protection, workflow integration, and human oversight.