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.
