Year: 2026
Type: article
Abstract: The global AI divide in healthcare is widening. While high-resource settings increasingly deploy sophisticated clinical AI, low-resource settings, where disease burdens are often greatest, remain underrepresented in training datasets and underserved by the infrastructure and expertise that underpin model development. AI models trained on narrow or geographically concentrated datasets consequently underperform in the very populations where they are most needed. Without deliberate efforts to advance AI as a digital public good, these technologies risk perpetuating rather than reducing health inequities. Privacy-enhancing technologies (PETs), though well-established, remain underutilized in global health. By enabling collaborative model development without the routine movement of sensitive individual-level data, PETs allow algorithms to learn from diverse populations while respecting data sovereignty, local and international regulations, and institutional policies. However, many PETs remain difficult to implement in settings with limited computational infrastructure, technical expertise, and workforce capacity. In this Perspective, we examine the PET landscape for health research and implementation, emphasizing that techniques must be matched to local capacity and use case. We also address emerging challenges as large language models enter healthcare, particularly where third-party Application Programming Interface (API)-based architectures conflict with data protection and sovereignty requirements. We propose four priorities for equitable implementation: governance that recognizes data sovereignty, guidelines that accommodate privacy-preserving collaboration, investment in lightweight and locally deployable AI infrastructure, and partnerships built on trust and mutual benefit. Technical solutions alone are insufficient; successful implementation also requires trust, mutual benefit, and alignment across diverse legal, cultural, and institutional contexts. Together, these measures can support more equitable development and deployment of AI for global health.
Source: npj Digital Medicine
Authors: Siqi Li, Chao Yan, Jasmine Chiat Ling Ong, Yilin Ning, Serim Kim, Wangxuan Fan, Katherine E. Brown, Iain Bee Huat Tan, Evelyn Yi Ting Wong, Qian Long, Zengxiang Li, Paul Michael Pronyk, Michael Barber, Lian Leng Low, Marcus Eng Hock Ong, Enrico Petretto, Hiang Khoon Tan, Melissa D. McCradden, Rui Yang, Rui Zhang, Lijing L. Yan, Daniel Shu Wei Ting, Bilal A. Mateen, Alastair K. Denniston, Bradley Malin, Nan Liu
Institutions: Duke-NUS Medical School, Vanderbilt University Medical Center, Singapore General Hospital, SingHealth, National University of Singapore, National Cancer Centre Singapore, Duke University, Duke Kunshan University, SingHealth Duke-NUS Academic Medical Centre, China Pharmaceutical University, Women's and Children's Health Network, Sickkids Research Institute, University of Minnesota, Peking University, Wuhan University, Singapore National Eye Center, Singapore Eye Research Institute, Stanford University, Program for Appropriate Technology in Health, University of Birmingham, University Hospitals Birmingham NHS Foundation Trust
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,090
OpenAlex ID: W7214172805