4.5 Article

Revolutionizing radiology with GPT-based models: Current applications, future possibilities and limitations of ChatGPT

Journal

DIAGNOSTIC AND INTERVENTIONAL IMAGING
Volume 104, Issue 6, Pages 269-274

Publisher

ELSEVIER MASSON, CORP OFF
DOI: 10.1016/j.diii.2023.02.003

Keywords

Artificial intelligence; ChatGPT; Generative pre -trained transformer (GPT); Radiology

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Artificial intelligence, specifically the use of generative pre-trained transformer (GPT)-based models, has the potential to revolutionize the field of radiology by improving accuracy, efficiency, and patient outcome. Current applications of GPT-based models include report generation, educational support, clinical decision support, patient communication, and data analysis. Further advancements and validation are required for full utilization of these models in radiology.
Artificial intelligence has demonstrated utility and is increasingly being used in the field of radiology. The use of generative pre-trained transformer (GPT)-based models has the potential to revolutionize the field of radiology, offering new possibilities for improving accuracy, efficiency, and patient outcome. Current applications of GPT-based models in radiology include report generation, educational support, clinical decision support, patient communication, and data analysis. As these models continue to advance and improve, it is likely that more innovative uses for GPT-based models in the field of radiology at large will be developed, further enhancing the role of technology in the diagnostic process. ChatGPT is a variant of GPT that is specifically fine-tuned for conversational language understanding and generation. This article reports some answers provided by ChatGPT to various questions that radiologists may have regarding ChatGPT and identifies the potential benefits ChatGPT may offer in their daily practice but also current limitations. Similar to other applications of artificial intelligence in the field of imaging, further formal validation of ChatGPT is required. & COPY; 2023 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.

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