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

Background: The increasing integration of artificial intelligence (AI) in health care, 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 ChatGPT and similar models. LLMs, designed for Natural Language Processing, exhibit promising capabilities in clinical decision-making, workflow optimization, education, and patient-centered care.

Educational Points: 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. The paper 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, the 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 inter-disciplinary collaboration are also emphasized. We advocate for a balanced approach, combining LLMs with computer vision AI models to address 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 health care delivery and innovation.

 

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Artikel online veröffentlicht:
02. April 2024