Digital twins in increasing diversity in clinical trials: A systematic review.

IF 4.5 2区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Journal of Biomedical Informatics Pub Date : 2025-09-01 Epub Date: 2025-08-08 DOI:10.1016/j.jbi.2025.104879
Abigail Tubbs, Enrique Alvarez Vazquez
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引用次数: 0

Abstract

The integration of digital twin (DT) technology and artificial intelligence (AI) into clinical trials holds transformative potential for addressing persistent inequities in participant representation. This systematic review evaluates the role of these technologies in improving diversity, particularly in racial, ethnic, gender, age, and socioeconomic dimensions, minimizing bias, and allowing personalized medicine in clinical research settings. Evidence from 90 studies reveals that digital twins offer dynamic simulation capabilities for trial design, while AI facilitates predictive analytics and recruitment optimization. However, implementation remains hindered by fragmented regulatory frameworks, biased datasets, and infrastructural disparities. Ethical concerns,including privacy, consent, and algorithmic opacity, further complicate the deployment. Inclusive data practices identified in the literature include the use of demographically representative training data, participatory data collection frameworks, and equity audits to detect and correct systemic bias. Fairness in AI and DT models is primarily operationalized through group fairness metrics such as demographic parity and equalized odds, along with fairness, aware model training and validation. Key gaps include the lack of global standards, underrepresentation in model training, and challenges in real-world adoption. To overcome these barriers, the review proposes actionable directions: developing inclusive data practices, harmonizing regulatory oversight, and embedding fairness into computational model design. By focusing on diversity as a design principle, AI and DT technologies can support a more equitable and generalizable future for clinical research.

数字双胞胎在临床试验中增加多样性:系统回顾。
将数字孪生(DT)技术和人工智能(AI)整合到临床试验中,对于解决参与者代表性方面持续存在的不平等问题具有变革性潜力。本系统综述评估了这些技术在改善多样性方面的作用,特别是在种族、民族、性别、年龄和社会经济方面,最大限度地减少偏见,并在临床研究环境中允许个性化医疗。来自90项研究的证据表明,数字双胞胎为试验设计提供了动态模拟能力,而人工智能则有助于预测分析和招聘优化。然而,实施仍然受到分散的监管框架、有偏见的数据集和基础设施差异的阻碍。伦理问题,包括隐私、同意和算法不透明,使部署进一步复杂化。文献中确定的包容性数据实践包括使用具有人口代表性的培训数据、参与式数据收集框架和公平审计来发现和纠正系统性偏见。AI和DT模型中的公平性主要通过群体公平性指标(如人口均等和均等几率)以及公平性、有意识的模型训练和验证来实现。主要的差距包括缺乏全球标准,模型训练中的代表性不足,以及在现实世界中采用的挑战。为了克服这些障碍,报告提出了可操作的方向:发展包容性数据实践,协调监管监督,将公平性嵌入计算模型设计。通过将多样性作为一项设计原则,人工智能和DT技术可以为临床研究提供更公平和更普遍的未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biomedical Informatics
Journal of Biomedical Informatics 医学-计算机:跨学科应用
CiteScore
8.90
自引率
6.70%
发文量
243
审稿时长
32 days
期刊介绍: The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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