Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence

Abstract

BackgroundRecent studies demonstrate the potential of Artificial Intelligence 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.ObjectiveThe present study undertakes a review of research on the current status of artificial intelligence (AI) to identify requirements, gaps, challenges, and possible strategies to strengthen the large, complex, and heterogeneous health systems in LMICs.DesignAfter introducing the general challenges developing countries face, the methodology of systematic reviews and the meta-analyses extension for scoping reviews (PRISMA-ScR) is introduced according to the preferred reporting items. Scopus and Web of Science databases were used to identify papers published between 2011–2022, from which we selected 151 eligible publications. Moreover, a narrative review was conducted to analyze the evidence in the literature about explicit evidence of strategies to overcome particular AI challenges in LMICs.ResultsThe 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 eight 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 (at least one per challenge) were identified.ConclusionResearch 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.Systematic review registration[https://rb.gy/frn2rz].

Impacts of the advancement in artificial intelligence on laboratory medicine in low‐ and middle‐income countries: Challenges and recommendations—A literature review

“…Using AI in laboratory medicine poses ethical and legal concerns, such as patient privacy, data protection, informed consent, and liability concerns. 4 Because LMICs may lack wellestablished policies and frameworks to address these problems, uncertainty and possible hazards may arise. To address difficulties with bias, transparency, responsibility, and liability, strong ethical principles and legislative frameworks tailored to AI in health care are required.…”

“…4,6 In addition to enhancing disease surveillance and enabling earlier disease detection and management, AI algorithms can also help with the interpretation of laboratory test data as well. 1,4 It is significant to note that overcoming numerous obstacles is necessary for the successful integration of AI in laboratory medicine in LMICs. These difficulties include the scarcity of data, the requirement for contextually and locally appropriate algorithms, moral issues, capacity building, and infrastructure development.…”

“…These nations frequently experience difficulties regarding health care, such as few resources, poor infrastructure, and a high burden of diseases. 4,6 AI technologies can solve these problems and enhance the delivery of health care in LMICs. For instance, AI can support clinical decision-making in resource-constrained environments, improve the accuracy of illness identification, and optimize diagnostic processes.…”

AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector

“…The general identified and reported challenges (C1-C11) include the following (Schoeman et al, 2017;Alami et al, 2020;Gwagwa et al, 2020aGwagwa et al, ,b, 2021How et al, 2020;Valle-Cruz et al, 2020;Arakpogun et al, 2021;Sobrino-García, 2021;Amankwah-Amoah and Lu, 2022;López et al, 2022;Zagabathuni and Zagabathuni, 2022;Eke, 2023) Despite the fact that the study was limited to investigating challenges that the Academia-Private sector faces in innovating responsible AI in Anglophone African countries, the recommended adoption strategies and solutions can be applied in other African countries. In general, the major contributions of this study are summarized as follows:…”

“…The general identified and reported challenges (C1-C11) include the following (Schoeman et al, 2017 ; Alami et al, 2020 ; Gwagwa et al, 2020a , b , 2021 ; How et al, 2020 ; Valle-Cruz et al, 2020 ; Arakpogun et al, 2021 ; Sobrino-García, 2021 ; Amankwah-Amoah and Lu, 2022 ; López et al, 2022 ; Zagabathuni and Zagabathuni, 2022 ; Eke, 2023 ) 1 :…”