Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence - López, Diego M.; Rico-Olarte, Carolina; Blobel, Bernd; Hullin, Carol (Frontiers in Medicine, 2022)
Background: Recent studies demonstrate the potential of Artificial Intelligence (AI) to support diagnosis, mortality assessment, and clinical decisions in low-and-middle-income countries (LMICs). However, explicit evidence of strategies to overcome the particular challenges for transformed health systems in these countries does not exist. Objective: The present study undertakes a review of research on the current status of AI to identify requirements, gaps, challenges, and possible strategies to strengthen the large, complex, and heterogeneous health systems in LMICs. Design: After introducing the general challenges developing countries face, the methodology of systematic reviews and the meta-analyses extension for scoping reviews (PRISMA-ScR) is introduced. Scopus and WOS databases were used to identify papers published between 2011-2022. We selected 151 eligible publications. Moreover, a narrative review was conducted to analyze the evidence of strategies to overcome particular AI challenges in LMICs. Results: The analysis of results was divided into two groups: primary studies, which include experimental studies or case studies using or deploying a specific AI solution (n=129), and secondary studies, including opinion papers, systematic reviews, and papers with strategies or guidelines (n=22). For both study groups, a descriptive statistical analysis was performed describing their technological contribution, data used, health context, and type of health interventions. For the secondary studies group, an in-deep narrative review was performed, identifying a set of 40 challenges gathered in 8 different categories: data quality, context awareness; regulation and legal frameworks; education and change resistance; financial resources; methodology; infrastructure and connectivity; and scalability. A total of 89 recommendations were identified. Conclusions: Research on applying AI and ML to healthcare interventions in LMICs is growing; however, apart from very well-described ML methods and algorithms, there are several challenges to be addressed to scale and mainstream experimental and pilot studies. The main challenges include improving the quality of existing data sources, training and modeling AI solutions based on contextual data; and implementing privacy, security, informed consent, ethical, liability, confidentiality, trust, equity, and accountability policies. Also, robust eHealth environments with trained stakeholders, methodological standards for data creation, research reporting, product certification, sustained investment in data sharing, infrastructures, and connectivity are necessary.