What Is Artificial Intelligence?

Abstract

This chapter explores the transformative role of artificial intelligence (AI) and machine learning (ML) in modern healthcare and medical training. We first outline the rapid progress of AI, fueled by the digitization of health data and advances in computing, which has led to significant shifts in how clinical data and knowledge are generated and applied. We briefly introduce supervised, unsupervised, and reinforcement learning, as well as recent breakthroughs in generative AI and large language models (LLMs). These technologies are increasingly integrated into clinical workflows for tasks such as diagnostic support in radiology and pathology, sepsis risk prediction, and automated clinical documentation. Crucially, these technologies impact both clinical practice and research, and therefore medical educators must prepare clinicians for an AI-augmented environment by fostering algorithmic literacy and an understanding of statistical bias, data quality, and ethical responsibility. Further, these technologies may augment and improve state-of-the-art medical training, the scale and specifics of which are being fleshed out in real time. We conclude by addressing the significant ethical, legal, and societal implications of AI, highlighting the need for transparency, accountability, and continuous monitoring to ensure equitable and safe deployment in clinical practice.