Machine learning and deep learning in internal medicine: demystifying concepts

Machine learning (ML) is a branch of artificial intelligence that is transforming clinical practice by providing tools capable of analyzing large volumes of data, identifying complex patterns, and generating predictions useful for medical decision-making. This article offers a practical and accessible introduction to key ML concepts for internists, addressing its application in tasks such as diagnosis, prognosis, and clinical management. The main types of learning (supervised, unsupervised, and reinforcement learning), the importance of data quality, and the systematic process for developing ML projects in medicine are described. Advanced approaches, such as neural networks and model explainability, are also explored. By integrating these tools, clinicians can improve diagnostic accuracy, personalize treatments, and optimize resources, always with a critical approach that respects medical ethics.