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CC BY 4.0 · The Arab Journal of Interventional Radiology
DOI: 10.1055/s-0044-1782663
Review Article

Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges

Taofeeq Oluwatosin Togunwa
1   Department of Radiology, College of Medicine, University of Ibadan, Oyo, Nigeria
2   College Research and Innovation Hub, University College Hospital, Ibadan, Oyo, Nigeria
,
Abdulquddus Ajibade
1   Department of Radiology, College of Medicine, University of Ibadan, Oyo, Nigeria
,
Christabel Uche-Orji
1   Department of Radiology, College of Medicine, University of Ibadan, Oyo, Nigeria
2   College Research and Innovation Hub, University College Hospital, Ibadan, Oyo, Nigeria
,
Richard Olatunji
1   Department of Radiology, College of Medicine, University of Ibadan, Oyo, Nigeria
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Abstract

The increasing integration of artificial intelligence (AI) in healthcare, particularly in vascular and interventional radiology (VIR), has opened avenues for enhanced efficiency and precision. This narrative review delves into the potential applications of large language models (LLMs) in VIR, with a focus on Chat Generative Pre-Trained Transformer (ChatGPT) and similar models. LLMs, designed for natural language processing, exhibit promising capabilities in clinical decision-making, workflow optimization, education, and patient-centered care. The discussion highlights LLMs' ability to analyze extensive medical literature, aiding radiologists in making informed decisions. Moreover, their role in improving clinical workflow, automating report generation, and intelligent patient scheduling is explored. This article also examines LLMs' impact on VIR education, presenting them as valuable tools for trainees. Additionally, the integration of LLMs into patient education processes is examined, highlighting their potential to enhance patient-centered care through simplified and accurate medical information dissemination. Despite these potentials, this paper discusses challenges and ethical considerations, including AI over-reliance, potential misinformation, and biases. The scarcity of comprehensive VIR datasets and the need for ongoing monitoring and interdisciplinary collaboration are also emphasized. Advocating for a balanced approach, the combination of LLMs with computer vision AI models addresses the inherently visual nature of VIR. Overall, while the widespread implementation of LLMs in VIR may be premature, their potential to improve various aspects of the discipline is undeniable. Recognizing challenges and ethical considerations, fostering collaboration, and adhering to ethical standards are essential for unlocking the full potential of LLMs in VIR, ushering in a new era of healthcare delivery and innovation.

Keywords

artificial intelligence - vascular and interventional radiology - large language models - machine learning - radiology - patient-centered care

Authors' Contribution

T.O.T. conceptualized the study. R.O. is the guarantor of the study. T.O.T., R.O., and A.A. were involved in methodology. T.O.T., R.O. A.A., and C.U-O. helped in providing resources, writing—original draft preparation and editing,. All authors have read and agreed to the final version of the manuscript.




Publication History

Article published online:
19 April 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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