Artificial Intelligence in Medical Informatics: From Algorithms to Clinical Practices - A Survey

Artificial intelligence is reshaping medical informatics from a discipline of data management into a science of integration, inference, and translation. As biomedical data proliferate across physiological, clinical, and molecular domains, AI functions as the integrative engine that transforms complexity into actionable understanding. In this review, we synthesize recent advances spanning data representation, algorithmic innovation, and clinical deployment, emphasizing the transition from isolated tasks to cohesive systems that link discovery and care. We highlight how advances in medical AI algorithms across clinical data, medical imaging, and multi-omics are beginning to converge with applications in clinical diagnosis, drug discovery, precision medicine, and surgery. Looking ahead, medical AI is moving toward a self-reflective and collaborative paradigm, where progress in multi-modalityity, trustworthiness, human–machine synergy, and ethical reasoning may allow intelligence to be woven into the pipeline of clinical practice and fulfill its translational promise.