Artificial intelligence (AI) is experiencing considerable growth in medicine, driven by the explosion of available biomedical data and the emergence of new algorithmic architectures. Applications are rapidly multiplying, from diagnostic assistance to disease progression prediction, paving the way for more personalized medicine. The recent advent of large language models, such as ChatGPT, has particularly interested the medical community, thanks to their ease of use, but also raised questions about their reliability in medical contexts. This review presents the fundamental concepts of medical AI, specifically distinguishing traditional discriminative approaches from new generative models. We detail the different exploitable data sources and methodological pitfalls to avoid during the development of these tools. Finally, we address the practical and ethical implications of this technological revolution, emphasizing the importance of the medical community's appropriation of these tools.