Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions

作者：Tugba Akinci D’Antonoli, Arnaldo Stanzione, Christian Bluethgen, Federica Vernuccio, Lorenzo Ugga, Michail E. Klontzas, Renato Cuocolo, Roberto Cannella, Burak Koçak · 发表于：Diagnostic and Interventional Radiology · 年份：2023 · DOI：10.4274/dir.2023.232417 · 被引用次数：220 · 研究领域：Artificial Intelligence in Healthcare and Education、Radiomics and Machine Learning in Medical Imaging、COVID-19 diagnosis using AI

With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions.