JMIR Publications

ABSTRACT

Background:

Artificial Intelligence (AI)-enabled systems are increasingly being positioned to address structural gaps in healthcare access, including in low- and middle-income countries (LMICs) facing workforce shortages and limited specialist availability. In particular, Large Language Models (LLMs), a type of AI model that generates and interprets text (e.g., GPT, Claude, and Gemini), are rapidly integrated into healthcare systems. Most research has focused on implementation in well-resourced settings, leaving LLM integration in low-and middle-income countries poorly understood. This study examines real-world LLM adoption in global health practice, focusing on the motivations, risks, and the infrastructural and organizational factors that shape implementation in low-and middle-income countries.

Objective:

This study aimed to examine how LLMs are implemented in real-world global health settings and to explore the motivations, perceived opportunities and risks, and organizational and infrastructural factors that shape their integration into health workflows and decision-making.

Methods:

We conducted semi-structured interviews with 17 professionals involved in the design, engineering, implementation, study, and oversight of LLM-based tools in global health. We recruited participants using purposive and snowball sampling to capture variation across role type and geographies, including South Asia, the Eastern Mediterranean, Africa, and multi-country global programs. We analyzed data using reflexive thematic analysis.

Results:

Five themes emerged. First, participants described LLMs as useful for reducing provider workload and expanding access to health information, but adoption was also shaped by funding pressures. Second, implementation was constrained by inadequate infrastructure, unreliable connectivity, reduced accuracy in low-resource languages, and data limitations. Third, local system integration required extensive sociotechnical effort, including co-design and linguistic customization. Fourth, organizations relied on multiple safety architectures and hybrid care pathways to safely deploy LLM tools in healthcare contexts. Fifth, organizations lacked clear outcome measures linking LLM use to health outcomes.

Conclusions:

LLM implementation in global health is shaped by healthcare gaps, funding availability, and substantial socio-technical work, including workflow integration, human oversight, and investment in local language data. Gaps in accountability, safety evaluation, and outcome measurement mean limited evidence for safe and equitable scale-up, highlighting the need for stronger governance, safety, and outcome-focused evaluation frameworks. Clinical Trial: NA