Contextual challenges in implementing artificial intelligence for healthcare in low-resource environments: insights...

Nigeria is the most populous country in Africa with the highest gross domestic product (GDP) as of 2022. However, Nigeria is burdened by significant health challenges including an extremely high maternal mortality ratio, inadequate human resources, poor healthcare infrastructure, and population-level poverty rates as high as 40%. Nigeria also has the highest reported prevalence of peripartum cardiomyopathy worldwide which contributes to maternal mortality. Unfortunately, the diagnosis of peripartum cardiomyopathy is often delayed and mortality rates following diagnosis are extremely high (approximately 50%). Thus, there is a huge unmet need for simple, effective, and accessible solutions for cardiomyopathy detection in this population. To address maternal mortality through screening and early diagnosis, we designed and conducted a randomized controlled clinical trial (NCT05438576) of an artificial intelligence (AI) technology in Nigeria. The objective of the study was to evaluate the impact of AI-guided screening on cardiomyopathy detection in obstetric patients. The study findings showed AI-guided screening doubled the detection of cardiomyopathy (defined as left ventricular ejection fraction <50%) when compared to usual care with a number needed to screen of 47. As we explore next steps in relation to deploying this technology for clinical use in Nigeria, we sought to gather contextual information and broadly share lessons learned from the recently completed trial. To that end, we convened a round table discussion with all study site investigators aimed at identifying site-specific contextual challenges related to the development and conduct of the study. The SPEC-AI Nigeria study is the first published randomized controlled clinical trial of a health AI intervention in Nigeria. Insights gained from this study can inform future AI intervention studies in clinical care, guide the development of implementation strategies to ensure effective interventions are successfully incorporated into clinical care, and provide a roadmap for key stakeholders to consider when evaluating AI-technologies for use in low-resource settings.