Medical AI refers to machine learning and related methods used to support clinical decisions, interpret medical data and improve healthcare workflows. A major focus is image analysis, where deep learning models classify or segment radiology, pathology and dermatology images. These systems can detect tumors, fractures, diabetic retinopathy or skin lesions, often reaching performance comparable to human specialists in controlled studies. Other models analyze electrocardiograms to flag arrhythmias, or use clinical and laboratory data to predict sepsis, mortality or hospital readmission.

Natural language processing is used to extract structured information from free text such as clinical notes, discharge summaries and radiology reports. This enables automated coding, cohort identification for research and real time decision support. Large language models are beginning to assist with documentation, triage and patient communication, though reliability and hallucinations remain concerns.

Key technical issues include dataset bias, domain shift and poor generalization to new hospitals, scanners or populations. Robust evaluation requires external validation, clinically meaningful endpoints and comparison with standard care rather than only with human readers. Interpretability methods such as saliency maps or feature importance aim to reveal why a model makes a prediction, but they can be misleading and do not guarantee safety.

Ethical and regulatory questions center on privacy, informed consent, accountability and transparency. There is debate over whether AI should act autonomously or remain a decision support tool with human oversight. Successful deployment depends on integrating systems into clinical workflows, continuous monitoring after implementation and collaboration between clinicians, data scientists and regulators to ensure that performance gains translate into real patient benefit.