医学影像基础模型时代正在临近：放射学中大规模生成式AI应用临床价值的范围综述
The Era of Foundation Models in Medical Imaging is Approaching : A Scoping Review of the Clinical Value of Large-Scale Generative AI Applications in Radiology
AI总结：
本综述系统回顾了15项研究，探讨大规模生成式AI在放射学报告生成与影像解读中的临床价值，指出其虽在特定领域表现优异但尚未超越放射科医生，并预示医学影像基础模型时代即将来临。
AI中文摘要：
放射科医生短缺引发的社会问题日益加剧，人工智能正被视为一种潜在的解决方案。近期涌现的大规模生成式AI已从大语言模型（LLMs）扩展至多模态模型，展现出彻底变革医学影像全流程的潜力。然而，目前缺乏关于其发展现状和未来挑战的全面综述。本范围综述遵循PCC指南，系统梳理了大规模生成式AI应用临床价值的现有文献。研究在PubMed、EMbase、IEEE-Xplore和Google Scholar四个数据库中进行了系统检索，并回顾了15项符合研究者设定的纳入/排除标准的研究。这些研究大多侧重于提高影像解读过程中特定环节的报告生成效率，或将报告翻译以辅助患者理解，而最新研究已扩展至执行直接解读的AI应用。所有研究均由临床医生进行定量评估，其中大多数使用了LLMs，仅有三项采用了多模态模型。LLMs和多模态模型在特定领域均表现出色，但在诊断性能上尚无任何模型超越放射科医生。多数研究采用了GPT，极少使用专门针对医学影像领域的模型。本研究深入探讨了医学影像领域基于大规模生成式AI的应用现状与局限性，提供了基础数据，并表明医学影像基础模型时代即将来临，这可能在不久的将来从根本上改变临床实践。
英文摘要：
Social problems stemming from the shortage of radiologists are intensifying, and artificial intelligence is being highlighted as a potential solution. Recently emerging large-scale generative AI has expanded from large language models (LLMs) to multi-modal models, showing potential to revolutionize the entire process of medical imaging. However, comprehensive reviews on their development status and future challenges are currently lacking. This scoping review systematically organizes existing literature on the clinical value of large-scale generative AI applications by following PCC guidelines. A systematic search was conducted across four databases: PubMed, EMbase, IEEE-Xplore, and Google Scholar, and 15 studies meeting the inclusion/exclusion criteria set by the researchers were reviewed. Most of these studies focused on improving the efficiency of report generation in specific parts of the interpretation process or on translating reports to aid patient understanding, with the latest studies extending to AI applications performing direct interpretations. All studies were quantitatively evaluated by clinicians, with most utilizing LLMs and only three employing multi-modal models. Both LLMs and multi-modal models showed excellent results in specific areas, but none yet outperformed radiologists in diagnostic performance. Most studies utilized GPT, with few using models specialized for the medical imaging domain. This study provides insights into the current state and limitations of large-scale generative AI-based applications in the medical imaging field, offering foundational data and suggesting that the era of medical imaging foundation models is on the horizon, which may fundamentally transform clinical practice in the near future.
