Iqbal Usman, Tanweer Afifa, Rahmanti Annisa Ristya, Greenfield David, Lee Leon Tsung-Ju, Li Yu-Chuan Jack
Institute for Evidence-Based Healthcare, Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia.
Evidence-Based Practice Professorial Unit, Gold Coast Hospital & Health Service (GCHHS), Gold Coast, QLD, Australia.
J Biomed Sci. 2025 May 7;32(1):45. doi: 10.1186/s12929-025-01131-z.
The emergence of Artificial Intelligence (AI), particularly Chat Generative Pre-Trained Transformer (ChatGPT), a Large Language Model (LLM), in healthcare promises to reshape patient care, clinical decision-making, and medical education. This review aims to synthesise research findings to consolidate the implications of ChatGPT integration in healthcare and identify research gaps.
The umbrella review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Cochrane Library, PubMed, Scopus, Web of Science, and Google Scholar were searched from inception until February 2024. Due to the heterogeneity of the included studies, no quantitative analysis was performed. Instead, information was extracted, summarised, synthesised, and presented in a narrative form. Two reviewers undertook title, abstract, and full text screening independently. The methodological quality and overall rating of the included reviews were assessed using the A Measurement Tool to Assess systematic Reviews (AMSTAR-2) checklist. The review examined 17 studies, comprising 15 systematic reviews and 2 meta-analyses, on ChatGPT in healthcare, revealing diverse focuses. The AMSTAR-2 assessment identified 5 moderate and 12 low-quality reviews, with deficiencies like study design justification and funding source reporting. The most reported theme that emerged was ChatGPT's use in disease diagnosis or clinical decision-making. While 82.4% of studies focused on its general usage, 17.6% explored unique topics like its role in medical examinations and conducting systematic reviews. Among these, 52.9% targeted general healthcare, with 41.2% focusing on specific domains like radiology, neurosurgery, gastroenterology, public health dentistry, and ophthalmology. ChatGPT's use for manuscript review or writing was mentioned in 17.6% of reviews. Promising applications include enhancing patient care and clinical decision-making, though ethical, legal, and accuracy concerns require cautious integration.
We summarise the identified areas in reviews regarding ChatGPT's transformative impact in healthcare, highlighting patient care, decision-making, and medical education. Emphasising the importance of ethical regulations and the involvement of policymakers, we urge further investigation to ensure the reliability of ChatGPT and to promote trust in healthcare and research.
人工智能（AI）的出现，尤其是大型语言模型（LLM）Chat生成式预训练变换器（ChatGPT），有望重塑医疗保健中的患者护理、临床决策和医学教育。本综述旨在综合研究结果，以巩固ChatGPT融入医疗保健的影响并识别研究空白。
本伞形综述遵循系统评价和Meta分析的首选报告项目（PRISMA）指南进行。从创刊到2024年2月，对考克兰图书馆、PubMed、Scopus、科学网和谷歌学术进行了检索。由于纳入研究的异质性，未进行定量分析。相反，信息被提取、总结、综合并以叙述形式呈现。两名评审员独立进行标题、摘要和全文筛选。使用评估系统评价的测量工具（AMSTAR-2）清单评估纳入综述的方法学质量和总体评分。该综述审查了17项关于ChatGPT在医疗保健中的研究，包括15项系统评价和2项Meta分析，揭示了不同的重点。AMSTAR-2评估确定了5项中等质量和12项低质量的综述，存在研究设计合理性和资金来源报告等缺陷。出现的最常报道的主题是ChatGPT在疾病诊断或临床决策中的应用。虽然82.4%的研究关注其一般用途，但17.6%探索了独特的主题，如它在医学检查和进行系统评价中的作用。其中，52.9%针对一般医疗保健，41.2%关注放射学、神经外科、胃肠病学、公共卫生牙科和眼科等特定领域。17.6%的综述提到了ChatGPT用于稿件评审或撰写。有前景的应用包括改善患者护理和临床决策，不过伦理、法律和准确性问题需要谨慎整合。
我们总结了综述中确定的关于ChatGPT对医疗保健变革性影响的领域，突出了患者护理、决策和医学教育。强调伦理规范和政策制定者参与的重要性，我们敦促进一步调查，以确保ChatGPT的可靠性，并促进对医疗保健和研究的信任。
