<p>Artificial intelligence (AI) is increasingly integrated into healthcare, and it aligns naturally with the 6P medicine model (predictive, preventive, personalized, participatory, precision, and public health). In this narrative review, we synthesize recent evidence on how AI methods-including machine learning, deep learning, large language models, and digital twins-support each “P”, and we highlight where evidence is mature versus still exploratory. Across the literature, the strongest evidence focuses on predictive, preventive, and precision applications (e.g., imaging, risk stratification, and treatment decision support). In contrast, participatory and public health applications are less consistently evaluated and introduce additional challenges around equity, trust, and governance. We propose a practical 6P-AI implementation blueprint that links common use cases to data requirements, workflow integration steps, evaluation designs, and post-deployment monitoring. Responsible adoption requires attention to data quality, privacy, bias mitigation, transparency, human oversight, and alignment with regulatory frameworks (e.g., the European Health Data Space and the EU AI Act).</p>