Generative artificial intelligence (AI) is emerging as a transformative tool in healthcare, and this review provides a foundational introduction for clinicians without technical backgrounds. The article explains that generative models differ from traditional discriminative systems by producing new synthetic data such as images, text, and multimodal outputs. It highlights key model families—including diffusion models, large language models, and large multimodal models—and describes their growing use in generating synthetic medical images, automating documentation, and producing educational audio-video content.
Beyond general applications, recent work shows that generative AI can support diagnostic tools, strengthen information retrieval through retrieval-augmented generation, and enable coordinated workflows across multiple AI agents. Despite this potential, the review emphasizes significant limitations, including knowledge gaps, the risk of hallucinated outputs, biases rooted in training data, and challenges linked to the opaque (“black box”) nature of these systems. It concludes that although generative AI will not replace physicians, understanding both its capabilities and its constraints will be essential for safely and effectively integrating these technologies into clinical practice.
Keywords: Generative AI, large language models, healthcare technology, diffusion models
Reference: https://pmc.ncbi.nlm.nih.gov/articles/PMC12185825/