Abhari Shahabeddin, Afshari Yasna, Fatehi Farhad, Salmani Hosna, Garavand Ali, Chumachenko Dmytro, Zakerabasali Somayyeh, Morita Plinio P
School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.
Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam.
Ann Med Surg (Lond). 2024 Nov 8;86(12):7094-7104. doi: 10.1097/MS9.0000000000002716. eCollection 2024 Dec.
Recent advancements in generative AI, exemplified by ChatGPT, hold promise for healthcare applications such as decision-making support, education, and patient engagement. However, rigorous evaluation is crucial to ensure reliability and safety in clinical contexts. This scoping review explores ChatGPT's role in clinical inquiry, focusing on its characteristics, applications, challenges, and evaluation.
This review, conducted in 2023, followed PRISMA-ScR guidelines (Supplemental Digital Content 1, http://links.lww.com/MS9/A636). Searches were performed across PubMed, Scopus, IEEE, Web of Science, Cochrane, and Google Scholar using relevant keywords. The review explored ChatGPT's effectiveness in various medical domains, evaluation methods, target users, and comparisons with other AI models. Data synthesis and analysis incorporated both quantitative and qualitative approaches.
Analysis of 41 academic studies highlights ChatGPT's potential in medical education, patient care, and decision support, though performance varies by medical specialty and linguistic context. GPT-3.5, frequently referenced in 26 studies, demonstrated adaptability across diverse scenarios. Challenges include limited access to official answer keys and inconsistent performance, underscoring the need for ongoing refinement. Evaluation methods, including expert comparisons and statistical analyses, provided significant insights into ChatGPT's efficacy. The identification of target users, such as medical educators and nonexpert clinicians, illustrates its broad applicability.
ChatGPT shows significant potential in enhancing clinical practice and medical education. Nevertheless, continuous refinement is essential for its successful integration into healthcare, aiming to improve patient care outcomes, and address the evolving needs of the medical community.
以ChatGPT为代表的生成式人工智能的最新进展为医疗保健应用带来了希望，如决策支持、教育和患者参与。然而，严格的评估对于确保临床环境中的可靠性和安全性至关重要。本范围综述探讨了ChatGPT在临床问诊中的作用，重点关注其特征、应用、挑战和评估。
本综述于2023年进行，遵循PRISMA-ScR指南（补充数字内容1，http://links.lww.com/MS9/A636）。使用相关关键词在PubMed、Scopus、IEEE、科学网、Cochrane和谷歌学术上进行搜索。该综述探讨了ChatGPT在各个医学领域的有效性、评估方法、目标用户以及与其他人工智能模型的比较。数据综合与分析采用了定量和定性方法。
对41项学术研究的分析突出了ChatGPT在医学教育、患者护理和决策支持方面的潜力，尽管其表现因医学专业和语言环境而异。在26项研究中经常被引用的GPT-3.5在不同场景中表现出适应性。挑战包括获取官方答案密钥的机会有限以及性能不一致，这凸显了持续改进的必要性。评估方法，包括专家比较和统计分析，为ChatGPT的功效提供了重要见解。目标用户的确定，如医学教育工作者和非专家临床医生，说明了其广泛的适用性。
ChatGPT在加强临床实践和医学教育方面显示出巨大潜力。然而，持续改进对于其成功融入医疗保健至关重要，旨在改善患者护理结果，并满足医学界不断变化的需求。
