Clinician's Guide to Artificial Intelligence: Technical Foundations of Machine Learning

Artificial intelligence (AI) is rapidly integrating into clinical practice, from imaging interpretation to decision support; however, many clinicians lack a foundational understanding of how AI systems are constructed, trained, and evaluated. This article presents a technical framework covering fundamental concepts of machine learning, including traditional algorithms, deep neural network architectures, data modalities, training paradigms, evaluation metrics, and model interpretability. By outlining key principles and practical considerations, we aim to empower health care professionals to critically assess AI tools, anticipate their limitations, and integrate these technologies safely and effectively into patient care.