Department of Radiology, St. James's Hospital, Dublin, Ireland.
Department of Surgery, St. James's Hospital, Dublin, Ireland.
J Med Imaging Radiat Oncol. 2024 Apr;68(3):257-264. doi: 10.1111/1754-9485.13621. Epub 2024 Jan 19.
This study aimed to comprehensively evaluate the current utilization and future potential of ChatGPT, an AI-based chat model, in the field of radiology. The primary focus is on its role in enhancing decision-making processes, optimizing workflow efficiency, and fostering interdisciplinary collaboration and teaching within healthcare. A systematic search was conducted in PubMed, EMBASE and Web of Science databases. Key aspects, such as its impact on complex decision-making, workflow enhancement and collaboration, were assessed. Limitations and challenges associated with ChatGPT implementation were also examined. Overall, six studies met the inclusion criteria and were included in our analysis. All studies were prospective in nature. A total of 551 chatGPT (version 3.0 to 4.0) assessment events were included in our analysis. Considering the generation of academic papers, ChatGPT was found to output data inaccuracies 80% of the time. When ChatGPT was asked questions regarding common interventional radiology procedures, it contained entirely incorrect information 45% of the time. ChatGPT was seen to better answer US board-style questions when lower order thinking was required (P = 0.002). Improvements were seen between chatGPT 3.5 and 4.0 in regard to imaging questions with accuracy rates of 61 versus 85%(P = 0.009). ChatGPT was observed to have an average translational ability score of 4.27/5 on the Likert scale regarding CT and MRI findings. ChatGPT demonstrates substantial potential to augment decision-making and optimizing workflow. While ChatGPT's promise is evident, thorough evaluation and validation are imperative before widespread adoption in the field of radiology.
本研究旨在全面评估基于人工智能的聊天模型 ChatGPT 在放射学领域的当前应用和未来潜力。主要关注它在增强决策过程、优化工作流程效率以及促进医疗保健领域跨学科合作和教学方面的作用。我们在 PubMed、EMBASE 和 Web of Science 数据库中进行了系统检索。评估了其在复杂决策、工作流程增强和协作方面的影响等关键方面。还检查了与 ChatGPT 实施相关的局限性和挑战。总的来说，有 6 项研究符合纳入标准并纳入我们的分析。所有研究都是前瞻性的。我们的分析共包括 551 次 ChatGPT（版本 3.0 到 4.0）评估事件。考虑到学术论文的生成，ChatGPT 输出数据不准确的情况占 80%。当 ChatGPT 被问及常见介入放射学程序的问题时，它有 45%的时间提供完全错误的信息。当需要较低层次的思维时，ChatGPT 更善于回答美国董事会风格的问题（P=0.002）。在涉及成像问题时，ChatGPT 3.5 和 4.0 之间的准确性分别为 61%和 85%（P=0.009）。ChatGPT 在观察到 CT 和 MRI 结果时，其平均翻译能力得分为 4.27/5（Likert 量表）。ChatGPT 在增强决策和优化工作流程方面具有很大的潜力。虽然 ChatGPT 的前景明显，但在放射学领域广泛采用之前，必须进行彻底的评估和验证。
