{"title":"数字双胞胎在临床试验中增加多样性:系统回顾。","authors":"Abigail Tubbs, Enrique Alvarez Vazquez","doi":"10.1016/j.jbi.2025.104879","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104879"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digital twins in increasing diversity in clinical trials: A systematic review.\",\"authors\":\"Abigail Tubbs, Enrique Alvarez Vazquez\",\"doi\":\"10.1016/j.jbi.2025.104879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":15263,\"journal\":{\"name\":\"Journal of Biomedical Informatics\",\"volume\":\" \",\"pages\":\"104879\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Biomedical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jbi.2025.104879\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/8/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biomedical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jbi.2025.104879","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/8 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Digital twins in increasing diversity in clinical trials: A systematic review.
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.
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.