How cross-border alliances can make AI deliver on SDG health goals

AI partnerships are a practical remedy: pooling data to improve model fairness and generalizability; sharing technical know-how to overcome workforce shortages; and enabling LMICs to adapt, not just import, AI tools. This collaborative model, they write, can bridge the gap between innovation and real-world access to quality care.

A new peer-reviewed perspective published in Healthcare argues that the fastest path to equitable, AI-enabled healthcare runs through international partnerships that transfer technology, share data responsibly, and build local capacity. The article warns that uneven infrastructure, fragmented regulation, and funding gaps risk entrenching inequality unless countries cooperate deliberately on shared rules and skills pipelines.

The study, titled International Partnerships in AI-Driven Healthcare: Opportunities and Challenges for Advancing the UN Sustainable Development Goals—A Perspective, focuses on SDG 3 (Good Health and Well-being), with direct links to SDG 9 (Industry, Innovation and Infrastructure) and SDG 10 (Reduced Inequalities).

Why this matters now: uneven AI adoption is widening health gaps

The authors argue that AI’s gains in diagnostics, treatment planning, and public-health decision support are real but highly uneven, mirroring broader disparities in digital infrastructure, talent, and data availability. In high-income countries, robust connectivity, large datasets, and trained workforces accelerate deployment, while low- and middle-income countries (LMICs) face structural barriers from connectivity shortfalls to a lack of representative datasets and in-country AI expertise.

The paper highlights stark funding imbalances as a core driver of these gaps. In a recent analysis, only a sliver of global noncommunicable-disease grant funding reached LMIC institutions despite their disproportionate disease burden, underscoring how Africa and Latin America struggle to access capital for AI and health research.

AI partnerships are a practical remedy: pooling data to improve model fairness and generalizability; sharing technical know-how to overcome workforce shortages; and enabling LMICs to adapt, not just import, AI tools. This collaborative model, they write, can bridge the gap between innovation and real-world access to quality care.

How partnerships work: three pillars and credible case studies

The authors organize effective collaboration around three pillars. First, technology transfer ensures AI tools are adapted to local health systems rather than copy-pasted from rich-country contexts. Second, ethical data sharing supports diverse, representative datasets and fairer models. Third, capacity building equips local institutions and professionals to implement and sustain AI solutions over time. Together, these pillars form a framework for turning technical progress into scalable, context-sensitive outcomes.

To ground the framework, the article surveys initiatives selected through a targeted review of peer-reviewed research, policy reports, and institutional sources published between 2020 and early 2024. Selection prioritized geographic diversity, multi-stakeholder collaboration, and evidence of impact or scalability. While not a systematic review, the process sought to minimize selection bias and present a representative view of current collaborations.

Illustrative examples include north–south academic alliances that train clinicians and data scientists in sub-Saharan Africa; cross-border programs led by major research labs in South Asia to build AI capacity for early disease detection and health-logistics use cases; and public-private efforts in India to deploy AI screening for diabetic retinopathy, coupled with training for local health workers. These initiatives show how technical transfer paired with skills development creates sustainable ecosystems rather than one-off pilots.

The paper also sheds light on global efforts during and after the pandemic. The AI4COVID collaboration used diverse, multi-country datasets to improve the robustness of models for screening and surveillance, while confronting cross-border privacy and security issues. UNICEF’s AI-powered maternal and child health programs demonstrate how predictive tools, frontline training, and resource-allocation analytics can reduce maternal and neonatal mortality in low-resource settings.

Across this portfolio, the through-line is partnership: co-develop datasets, train local teams, and embed tools in the workflows of health ministries, NGOs, and providers so benefits endure after international partners step back.

What stands in the way and what needs to happen next

The authors identify a cluster of obstacles that routinely stall or shrink AI benefits in healthcare. Regulatory fragmentation complicates cross-border data governance and model deployment; data-privacy regimes differ widely, with stringent laws in some regions clashing with limited rules elsewhere; and the digital divide, from bandwidth to cloud infrastructure, limits the use of advanced tools in many LMICs.

They argue for practical fixes that governments and partners can adopt now. Harmonize rules for anonymization, interoperability, and algorithmic transparency to enable lawful, trusted sharing; expand connectivity and digital health infrastructure; and invest in digital literacy so clinicians understand both the strengths and limits of AI tools. These steps tie directly to SDG 9 and SDG 10 by building resilient infrastructure and reducing inequalities in access to innovation.

The paper notes that widely used reference frameworks already exist to help structure collaboration, ranging from the Global Alliance for Genomics and Health’s guidance on responsible data sharing to health-data-space initiatives that aim to standardize access and control. Adopting such models can accelerate trust and interoperability across borders.

But policy alignment alone won’t close the gap. The authors call for capacity-building as a first-order outcome of partnerships: technical training paired with ethical and clinical education; funding for local research; and development of region-specific models co-designed with local communities so systems are appropriate for context and sustainable beyond grant cycles.

Finally, donors and investors need to correct the funding imbalance by allocating a larger share of research and innovation capital to LMIC institutions. Without this shift, the paper suggests, global AI health collaborations will struggle to produce equitable results at scale.