AI is changing how health services are delivered in many high-income settings, particularly in specialty care (eg, radiology and pathology). This development has been facilitated by the growing availability of large datasets and novel analytical methods that rely on such datasets. Concurrent advances in information technology (IT) infrastructure and mobile computing power have raised hopes that AI might also provide opportunities to address health challenges in LMICs. These challenges, including acute health workforce shortages and weak public health surveillance systems, undermine global progress towards achieving the health-related sustainable development goals (SDGs). Although not unique to such countries, these challenges are particularly relevant given their contribution to morbidity and mortality.
AI-driven health technologies could be used to address many of these and other system-related challenges. For example, in some settings, AI-driven interventions have supplemented clinical decision making towards reducing the workload of health workers. New developments in AI have also helped to identify disease outbreaks earlier than traditional approaches, thereby supporting more timely programme planning and policy making. Although these interventions provide promise, there remain several ethical, regulatory, and practical issues that require guidance before scale-up or widespread deployment in low and middle-income settings.