Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges
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...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-07-01
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| Series: | The Arab Journal of Interventional Radiology |
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| Online Access: | http://www.thieme-connect.de/DOI/DOI?10.1055/s-0044-1782663 |
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| author | Taofeeq Oluwatosin Togunwa Abdulquddus Ajibade Christabel Uche-Orji Richard Olatunji |
| author_facet | Taofeeq Oluwatosin Togunwa Abdulquddus Ajibade Christabel Uche-Orji Richard Olatunji |
| author_sort | Taofeeq Oluwatosin Togunwa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba0b0e43828c47ea8ef1cebbc2ee841e |
| institution | Kabale University |
| issn | 2542-7075 2542-7083 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | The Arab Journal of Interventional Radiology |
| spelling | doaj-art-ba0b0e43828c47ea8ef1cebbc2ee841e2025-08-20T03:57:44ZengWolters Kluwer Medknow PublicationsThe Arab Journal of Interventional Radiology2542-70752542-70832024-07-01080206306910.1055/s-0044-1782663Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and ChallengesTaofeeq Oluwatosin Togunwa0Abdulquddus Ajibade1Christabel Uche-Orji2Richard Olatunji3Department of Radiology, College of Medicine, University of Ibadan, Oyo, NigeriaDepartment of Radiology, College of Medicine, University of Ibadan, Oyo, NigeriaDepartment of Radiology, College of Medicine, University of Ibadan, Oyo, NigeriaDepartment of Radiology, College of Medicine, University of Ibadan, Oyo, NigeriaThe 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.http://www.thieme-connect.de/DOI/DOI?10.1055/s-0044-1782663artificial intelligencevascular and interventional radiologylarge language modelsmachine learningradiologypatient-centered care |
| spellingShingle | Taofeeq Oluwatosin Togunwa Abdulquddus Ajibade Christabel Uche-Orji Richard Olatunji Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges The Arab Journal of Interventional Radiology artificial intelligence vascular and interventional radiology large language models machine learning radiology patient-centered care |
| title | Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges |
| title_full | Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges |
| title_fullStr | Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges |
| title_full_unstemmed | Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges |
| title_short | Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges |
| title_sort | exploring the potentials of large language models in vascular and interventional radiology opportunities and challenges |
| topic | artificial intelligence vascular and interventional radiology large language models machine learning radiology patient-centered care |
| url | http://www.thieme-connect.de/DOI/DOI?10.1055/s-0044-1782663 |
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