IPM Take
This is the right warning at the right time. AI is often sold as a shortcut for overstretched diabetes systems, especially in low resource settings. But the article makes clear that AI cannot compensate for weak data, fragmented infrastructure, poor validation or unclear regulation. For IPM, the key message is simple: AI can support equity in diabetes care only if it is built around local health system reality, not imported as a universal technical fix.
Executive Summary
A 2026 Lancet Diabetes & Endocrinology Personal View examines whether AI can help close gaps in primary diabetes care in low and middle income countries. The authors identify opportunities in screening, risk prediction, monitoring and personalised management of diabetes and its complications. They also highlight major barriers: infrastructure deficits, data fragmentation, equity and inclusivity challenges, limited prospective validation, acceptability, sustainability and regulatory oversight.
Why it matters
- Policymakers: Need to treat AI diabetes tools as implementation infrastructure, not only software procurement.
- Public authorities: Should require local validation, data governance and monitoring before scaling tools in primary care.
- Clinicians / patients: Need AI tools that support real care decisions, fit local workflows and do not widen access gaps.
Previously, AI in diabetes was often discussed through high income digital health markets, advanced devices and specialist analytics. This article shifts attention to primary care in low and middle income countries, where diabetes burden is rising and health system capacity is often limited.
What has changed is the policy framing. AI is presented as a possible enabler, but not as a stand-alone solution. The affected population includes people living with or at risk of diabetes in settings where primary care teams may lack resources, data systems, trained personnel and specialist support.
The article identifies real opportunities. AI could support screening, risk prediction, monitoring and personalised management. But those benefits depend on conditions that are often missing: interoperable data, local datasets, prospective validation, clinician trust, patient acceptability, regulatory oversight and sustainable financing.
The implementation message is sharp: AI diabetes tools should not be scaled simply because they are promising. They need local validation, governance, clinician trust, patient acceptability and sustainable infrastructure. Otherwise, AI could widen the same gaps it claims to close.
