Not surprisingly, most developments in AI thus far have come in the more developed parts of the world, often referred to as HIC’s, or High-Income Countries.

Also not surprisingly, it’s in the “other parts”of the world – the LMIC’s, or Low-and-Middle Income Countries – that AI could have the most comprehensive impact on healthcare and its delivery.

In a recent article at Science Magazine, Ahmed Hosny, and Hugo Aerts, both of Harvard Medical School, among other affiliations, take an in-depth look at the potential of, and challenges facing, AI in LMIC’s.  

For starters, they note that health conditions in the two demographic categories are actually converging, “as indicated by the recent shift of the global disease burden from infectious diseases to chronic noncommunicable diseases (NCDs, including cancer, cardiovascular disease, and diabetes). Both contexts also face similar challenges, such as physician burnout due to work-related stress, inefficiencies in clinical workflows, inaccuracies in diagnostic tests, and increases in hospital-acquired infections.”

Much of the similarity ends there, however, as in LMICs a larger range of basic needs – including health care workforce shortages, patient access to drugs, diagnostic imaging hardware, and surgical infrastructures – remain unmet. Indeed, they point out, “when equipment is available, LMICs often lack the technical expertise needed to operate, maintain, and repair it. As a result, 40% of medical equipment in LMICs is out of service.”

While many LMICs have undertaken substantial healthcare spending, “saving millions of lives by improving access to clean water, vaccinations, and HIV treatments,’ the authors point to three “application areas” where the use of AI could yield substantial overall gains.

“The first includes AI-powered low-cost tools running on smartphones or portable instruments. These mainly address common diseases and are operated by nonspecialist community health workers (CHWs) in off-site locations, including local centers and households. . . With increasing smartphone penetration, patient-facing AI applications may guide lifestyle and nutrition, allow symptom self-assessment, and provide advice during pregnancy or recovery periods—ultimately allowing patients to take control of their health and reducing the burden on limited health systems.”

The next application area is more specific, focusing on the goal of supporting clinical decision-making. “AI may allow nonspecialized primary care physicians to perform specialized tasks including reading diagnostic radiology and pathology images, only referring to specialists if necessary. AI tools may also help provide specialists with expert knowledge across multiple subspecialties.”

Finally, there’s AI potential to enhance population health efforts in LMICs, helping “public agencies to realize cause-and-effect relationships, appropriately allocate the often limited resources, and ultimately mitigate the progression of epidemics.”

There is, of course, no shortage of obstacles to these and other applications, but the authors argue the long-term benefits clearly outweigh myriad short-term challenges.

Ultimately, they say, “AI interventions in LMICs should be initiated, owned, and administered by local stakeholders—with HICs providing funding, expertise, and advice when needed. AI literacy may be included in existing global health educational programs to raise awareness about its capabilities and pitfalls. Empowering local technical AI talent will also be crucial, and may be accelerated through high-quality free educational online resources. AI implementation will (also) require rethinking existing regulatory frameworks.”