利用 GPT-4 推进放射学:临床应用、患者参与、研究和学习方面的创新

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Sadhana Kalidindi , Janani Baradwaj
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引用次数: 0

摘要

人工智能(AI)在医疗保健领域,尤其是放射学领域的快速发展,凸显了一个以提高诊断精确度、增加患者参与度和简化临床工作流程为特征的变革时代的潜力。在这一变革的核心中,大型语言模型(如生成式预训练转换器 4 (GPT-4))是关键的发展之一,通过协助生成和总结放射学报告、辅助鉴别诊断和推荐循证治疗,将其整合到放射学实践中可能预示着一次重大飞跃。本综述以 GPT-4 为例,深入探讨了大语言模型在放射学中的多方面潜在应用,包括提高诊断准确性和报告效率,以及将复杂的医学发现转化为患者友好的摘要。综述承认了部署人工智能技术所固有的伦理、隐私和技术挑战,强调了仔细监督、验证和遵守监管标准的重要性。通过对 GPT-4 在放射学中的潜力和隐患进行平衡论述,文章旨在全面概述这些模式如何有可能重塑放射学服务的未来,促进患者护理、教育方法和临床研究的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing radiology with GPT-4: Innovations in clinical applications, patient engagement, research, and learning

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.

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来源期刊
European Journal of Radiology Open
European Journal of Radiology Open Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.10
自引率
5.00%
发文量
55
审稿时长
51 days
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