Artificial Intelligence (AI) for Health Research in Low-Resource Settings Globally

Artificial Intelligence (AI) for Health Research in Low-Resource Settings Globally is sponsored by National Institutes of Health (NIH), Fogarty International Center (FIC). This initiative promotes ethical, responsible, and impactful AI use for health research in low-resource settings by fostering innovations while proactively addressing the technology's environmental and societal risks.

Artificial Intelligence (AI) for Health Research in Low-Resource Settings Globally - Fogarty International Center @ NIH Artificial Intelligence (AI) for Health Research in Low-Resource Settings Globally Photo credit: Yuri Arcurs / People Images Artificial Intelligence (AI) is at a critical juncture.

Recent advancements highlight its potential to deliver cost-effective, scalable solutions for preventable morbidity and mortality associated with communicable- and non-communicable diseases, maternal and child health, mental health, and injuries. From disease surveillance and diagnostics to strengthening health systems, ethical and responsible use of AI promises to address persistent health challenges facing all nations.

At the same time, NIH investments in AI for health research can strengthen health security by improving the capacity to detect and mitigate health and cybersecurity threats before they escalate. They can foster innovations that benefit low-resource settings worldwide, including under-served areas of the US.

Furthermore, these investments can enable scientific investigations of the potential for the technology to better meet local health needs and priorities.

The Fogarty International Center (FIC) Artificial Intelligence (AI) for Health Research project aims to promote ethical, responsible, and impactful AI use for health research in low-resource settings by fostering innovations while proactively addressing the technology’s environmental and societal risks.

This project builds on recent NIH initiatives (e.g., DS-I Africa, Bridge 2AI), among others, and strives to accelerate, facilitate, and coordinate scientific investigations of AI’s use for addressing persistent health challenges that span geographic and economic contexts.

Mapping the NIH Landscape of AI for Health Research in Low-Resource Settings To ensure AI delivers meaningful and measurable results from scientific applications for health in low-resource settings, we must understand where and how it is used, as well as key ethical and social considerations necessary to build community trust.

FIC is conducting a landscape analysis to: Characterize recent research on AI for health in low-resource settings—what’s working, where, and for whom? Analyze the literature and NIH’s AI for health science portfolio for research gaps and opportunities.

Highlight AI-driven innovations from low-resource settings that broadly inform best practices worldwide (e.g., cancer surveillance and predictive modeling, optimizing treatment planning and outcomes, streamlining health system workflows).

Building a Scientific Network of AI & Global Health Scientists The ethical and responsible investigation and use of AI-driven health solutions requires scientific collaboration across communities, disciplines, sectors, and countries.

Based on our current understanding of, and subject to, evolving scientific findings and health needs, FIC may explore the benefits of a network that brings together scientific partners and collaborators to: Connect research with real-world, high-priority health challenges. Promote collaboration within NIH and beyond to support AI for health research in low-resource settings.

Engage public and private sector partners to promote the ethical and responsible use of evidence-based AI for health. Gather experts via forums, workshops, and working groups to stimulate new collaborations. Encourage knowledge-sharing through publications, guidelines, and other resources.

Photo credit: Nuttapong punna Strengthening Capacity in AI for Health Research in Low-Resource Settings The benefits of AI in clinical and population health should be accessible to everyone.

To strengthen the ability of AI health research to address local needs and priorities in low-resource settings globally, Fogarty is exploring opportunities to build on current initiatives that strengthen capacity in AI for health science by: Identifying cross-disciplinary opportunities to link researchers and policymakers with scalable AI health science innovations.

Enhancing opportunities to improve access to data and computational resources, research capacity, and the ethical implementation of AI for health in low-resource settings. Scaling Evidence-based AI Solutions for Health Impact AI-applications for health must be developed, tested, refined, and scaled to drive sustainable improvements and advances.

To ensure a data-driven approach that builds upon existing use cases, FIC is working to: Highlight evidence of ethical and responsible AI-driven health science advances across geographic and economic contexts. Present case studies of successful AI innovations in low-resource settings. Disseminate best practices via reports, policy briefs, and online knowledge hubs.

Emphasize AI-driven innovations for global health security, such as outbreak detection and predictive modeling. This initiative is just starting, but we are establishing the foundation for a sustainable, collaborative effort to shape the future of AI in health research for low-resource settings worldwide. Division of International Science Policy, Planning, and Evaluation (DISPPE) U.S. National Institutes of Health (NIH)