A Current Review of Generative AI in Medicine: Core Concepts, Applications, and Current Limitations.

Abstract

Purpose of review: This review aims to offer a foundational overview of Generative Artificial Intelligence (AI) for healthcare professionals without an engineering background. It seeks to aid their understanding of Generative AI's current capabilities, applications, and limitations within the medical field.

Recent findings: Generative AI models, distinct from discriminative models, are designed to create novel synthetic data. Key model families discussed include diffusion models for generating images and videos, Large Language Models (LLMs) for text, and Large Multimodal Models (LMMs) capable of processing multiple data types. Recent applications in healthcare are diverse, encompassing general uses like generating synthetic medical images, automating clinical documentation, and creating synthetic audio/video for training. More specialized applications include leveraging Generative AI models as backbones for diagnostic aids, enhancing information retrieval through Retrieval-Augmented Generation (RAG) pipelines, and coordinating multiple AI agents in complex workflows. Generative AI holds significant transformative potential in medicine, enhancing capabilities across imaging, documentation, education, and decision support. However, its integration faces substantial challenges, including models' knowledge limitations, the risk of generating incorrect or uncertain "hallucinated" outputs, inherent biases from training data, difficulty in interpreting model reasoning ("black box" nature), and navigating complex regulatory and ethical issues. This review offers a balanced perspective, acknowledging both the promise and the hurdles. While Generative AI is unlikely to fully replace physicians, understanding and leveraging these technologies will be crucial for medical professionals navigating the evolving healthcare landscape.