Precision medicine aims to tailor healthcare decisions and interventions to the unique biological and clinical characteristics of each patient. The recent convergence of artificial intelligence (AI) with advances in digital health, omics, and big data analytics has accelerated progress toward this goal. AI technologies – particularly machine learning, deep learning, natural language processing and generative large language models – enable the rapid and meaningful analysis of complex biomedical datasets, supporting more individualized care.

In this narrative review, we provide an accessible overview of the core principles of AI for healthcare professionals and explore its practical applications across the spectrum of precision medicine. Real-world examples highlight how AI is being used to enhance early diagnosis, guide treatment selection, support disease prevention, and even contribute directly to therapeutic interventions. Alongside these advances, we discuss critical limitations and challenges, including ethical considerations, algorithmic bias, data privacy concerns, environmental impact, and practical barriers to clinical implementation.

This review offers both an introduction to AI and a practical overview of how it is being used, and where its limitations lie, in precision medicine, with the goal of helping healthcare professionals understand these evolving tools and use them efficiently and responsibly in clinical practice.