AbstractArtificial intelligence (AI) has been extensively researched in medicine, but its practical application remains limited. Meanwhile, there are various disparities in existing AI-enabled clinical studies, which pose a challenge to global health equity. In this study, we conducted an in-depth analysis of the geo-economic distribution of 159 AI-enabled clinical studies, as well as the gender disparities among these studies.

人工智能（AI）在医学上得到了广泛的研究，但其实际应用仍然有限。同时，现有的人工智能临床研究存在各种差异，这对全球健康公平构成了挑战。在这项研究中，我们对159项人工智能临床研究的地理经济分布以及这些研究之间的性别差异进行了深入分析。

We aim to reveal these disparities from a global literature perspective, thus highlighting the need for equitable access to medical AI technologies..

我们的目标是从全球文献的角度揭示这些差异，从而强调公平获得医疗AI技术的必要性。。

In the rapidly developing field of healthcare, artificial intelligence (AI) has emerged as a pivotal force driving innovation in clinical research and improving the efficiency of clinical studies1,2,3,4,5,6. However, despite rapid technological advancements, its practical application in clinical settings remains limited.

在快速发展的医疗保健领域，人工智能（AI）已成为推动临床研究创新和提高临床研究效率的关键力量1,2,3,4,5,6。然而，尽管技术进步迅速，但其在临床环境中的实际应用仍然有限。

Concurrently, there are complex disparities in current AI-enabled clinical studies. These disparities, which include data and algorithms, participants and subjects, and access to cutting-edge technologies7,8,9,10, challenge the equitable implementation of AI solutions11,12.We identified 159 clinical studies of AI-enabled applications from Embase, MEDLINE, and CINAHL through a systematic review.

同时，当前启用AI的临床研究存在复杂的差异。这些差异，包括数据和算法，参与者和受试者，以及对尖端技术的获取7,8,9,10，挑战了人工智能解决方案的公平实施11,12。我们确定了159项来自Embase，MEDLINE和CINAHL的人工智能应用的临床研究。通过系统评价。

Among these studies, 109 were conducted in hospital settings, while 50 took place in non-hospital environments. Notably, 51.6% (82/159) of the studies utilized AI for treatment and management, and 40.9% (65/159) focused on AI-assisted diagnosis, with a significant portion related to gastroenterology (see Supplementary Table 1).

在这些研究中，109项是在医院环境中进行的，而50项是在非医院环境中进行的。值得注意的是，51.6%（82/159）的研究利用AI进行治疗和管理，40.9%（65/159）的研究侧重于AI辅助诊断，其中很大一部分与胃肠病学有关（见补充表1）。

Moreover, 5.0% (8/159) of the studies applied AI for prognosis, and 2.5% (4/159) studies explored its use in patient education. In this study, we primarily analyzed the geo-economic distributions as well as the gender disparities among the study subjects.As depicted in Fig. 1a, the majority of studies were conducted in North America, Europe, and East Asia, with the United States (44 studies) and China (43 studies) leading.

此外，5.0%（8/159）的研究将AI应用于预后，2.5%（4/159）的研究探索了其在患者教育中的应用。。如图1a所示，大多数研究是在北美，欧洲和东亚进行的，美国（44项研究）和中国（43项研究）领先。

A significant portion (74.0%) of the clinical studies were implemented in high-income countries, 23.7% in upper-middle-income countries, 1.7% in lower-middle-income countries, and only one clinical study was conducted in low-income countries (i.e., Mozambique), as shown in Fig. 1b. Meanwhile, we analyzed the geo-economic.

临床研究的很大一部分（74.0%）在高收入国家实施，中高收入国家为23.7%，中低收入国家为1.7%，低收入国家（即莫桑比克）仅进行了一项临床研究，如图1b所示。。

Data availability

数据可用性

The data used in the manuscript can be found in Supplementary Material.

手稿中使用的数据可以在补充材料中找到。

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Hamadeh，N.等人，《按收入水平划分的世界银行新国家分类：2022-2023》（世界银行博客，2022年）。下载参考文献致谢这项工作得到了新加坡卫生部资助的杜克国立大学签名研究计划的支持。本材料中表达的任何意见，发现和结论或建议均为作者的意见，不反映卫生部的观点。

Additionally, we appreciate Jonathan Liew’s contribution to information extraction during the revision stage.Author informationAuthors and AffiliationsCentre for Quantitative Medicine, Duke-NUS Medical School, Singapore, SingaporeRui Yang, Sabarinath Vinod Nair, Yuhe Ke, Danny D’Agostino, Mingxuan Liu, Yilin Ning & Nan LiuDepartment of Anesthesiology, Singapore General Hospital, Singapore, SingaporeYuhe KeProgramme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, SingaporeNan LiuNUS Artificial Intelligence Institute, National University of Singapore, Singapore, SingaporeNan LiuAuthorsRui YangView author publicationsYou can also search for this author in.

此外，我们感谢乔纳森·刘在修订阶段对信息提取的贡献。作者信息新加坡国立杜克大学医学院定量医学的作者和附属机构Tre，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学人工智能研究所，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学医学院，新加坡国立杜克大学，新加坡国立杜克大学，新加坡国立杜克大学，新加坡国立杜克大学，新加坡国立杜克大学，新加坡国立杜克大学，新加坡国立杜克大学，新加坡国立杜克大学，新加坡国立杜克大学。

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PubMed Google ScholarContributionsN.L. conceived the study. R.Y., S.V.N., M.L., Y.N., and N.L. designed the study. R.Y., S.V.N., Y.K., and D.D. collected data. R.Y. conducted data analyses. R.Y. and S.V.N. drafted the manuscript, with further development by N.L. N.L. supervised the study.

PubMed谷歌学术贡献。五十、 构思了这项研究。R、 Y.，S.V.N.，M.L.，Y.N。和N.L.设计了这项研究。R、 Y.，S.V.N.，Y.K。和D.D.收集了数据。R、 Y.进行了数据分析。R、 。

All authors contributed to the revision of the manuscript and approval of the final version.Corresponding authorCorrespondence to.

所有作者都为稿件的修订和最终版本的批准做出了贡献。对应作者对应。

Nan Liu.Ethics declarations

刘楠。道德宣言

Competing interests

相互竞争的利益

N.L. is an Editorial Board Member for npj Digital Medicine. He played no role in the peer review of this manuscript. The remaining authors declare that there are no other financial or non-financial competing interests.

N、 L.是npj数字医学的编辑委员会成员。他在这份手稿的同行评审中没有发挥任何作用。其余作者声明，没有其他财务或非财务竞争利益。

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Reprints and permissionsAbout this articleCite this articleYang, R., Nair, S.V., Ke, Y. et al. Disparities in clinical studies of AI enabled applications from a global perspective.

转载和许可本文引用本文Yang，R.，Nair，S.V.，Ke，Y。等人。从全球角度来看，人工智能应用的临床研究存在差异。

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