Advancing radiology with GPT-4: Innovations in clinical applications, patient engagement, research, and learning

Abstract

The rapid evolution of artificial intelligence (AI) in healthcare, particularly in radiology, underscores a transformative era marked by a potential for enhanced diagnostic precision, increased patient engagement, and streamlined clinical workflows. Amongst the key developments at the heart of this transformation are Large Language Models like the Generative Pre-trained Transformer 4 (GPT-4), whose integration into radiological practices could potentially herald a significant leap by assisting in the generation and summarization of radiology reports, aiding in differential diagnoses, and recommending evidence-based treatments. This review delves into the multifaceted potential applications of Large Language Models within radiology, using GPT-4 as an example, from improving diagnostic accuracy and reporting efficiency to translating complex medical findings into patient-friendly summaries. The review acknowledges the ethical, privacy, and technical challenges inherent in deploying AI technologies, emphasizing the importance of careful oversight, validation, and adherence to regulatory standards. Through a balanced discourse on the potential and pitfalls of GPT-4 in radiology, the article aims to provide a comprehensive overview of how these models have the potential to reshape the future of radiological services, fostering improvements in patient care, educational methodologies, and clinical research.