BACKGROUND: Recent 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. OBJECTIVE: The 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. 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 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. 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 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. CONCLUSION: 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. SYSTEMATIC REVIEW REGISTRATION: [https://rb.gy/frn2rz].

1st Medical Faculty Charles University Prague Prague Czechia

Data Governance Unit Victoria Legal Aid Melbourne VIC Australia

Digital Innovation Center of Latin America Temuco Chile

eHealth Competence Center Bavaria Deggendorf Institute of Technology Deggendorf Germany

Medical Faculty University of Regensburg Regensburg Germany

Research Group in Telematics Engineering Telematics Department University of Cauca Popayán Colombia

Zobrazit více v PubMed

Blobel B, Oemig F, Ruotsalainen P, Lopez DM. Transformation of health and social care systems—an interdisciplinary approach toward a foundational architecture. Front. Med. (2022) 9:802487. 10.3389/fmed.2022.802487 PubMed DOI PMC

World Health Organization [WHO]. Ethics and Governance of Artificial Intelligence for Health: WHO Guidance. Geneva: World Health Organization; (2021).

United Nations [UN]. Report of the Secretary-General on SDG progress. Special edition. New York City, 2019. (2019). Available online at: https://sustainabledevelopment.un.org/content/documents/24978Report_of_the_SG_on_SDG_Progress_2019.pdf (accessed October 20, 2022).

Braa J, Blobel B. Strategies for developing health information systems in developing countries. In: Khakhar D. editor. WITFOR 2003 White Book. Laxenburg: IFIP Press; (2003). p. 175–219.

Braa J, Hanseth O, Heywood A, Mohammed W, Shaw V. Developing health information systems in developing countries: the flexible standards strategy. MIS Q. (2007) 31:381–402. 10.2307/25148796 DOI

Blobel B, Ruotsalainen P, Brochhausen M. Autonomous systems and artificial intelligence - hype or prerequisite for P5 medicine? Stud Health Technol Inform. (2021) 285:3–14. 10.3233/SHTI210567 PubMed DOI

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. (2019) 25:44–56. 10.1038/s41591-018-0300-7 PubMed DOI

Alami H, Rivard L, Lehoux P, Hoffman SJ, Cadeddu SBM, Savoldelli M, et al. Artificial intelligence in health care: laying the foundation for responsible, sustainable, and inclusive innovation in low- and middle-income countries. Glob Health. (2020) 16:52. 10.1186/s12992-020-00584-1 PubMed DOI PMC

Poole DL, Mackworth AK. Artificial Intelligence: Foundations of Computational Agents. 2nd ed. Cambridge: Cambridge University Press; (2017). p. 760. 10.1017/9781108164085 DOI

Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health. (2018) 3:e000798. 10.1136/bmjgh-2018-000798 PubMed DOI PMC

Zhang X, Pérez-Stable EJ, Bourne PE, Peprah E, Duru OK, Breen N, et al. Big data science: opportunities and challenges to address minority health and health disparities in the 21st century. Ethn Dis. (2017) 27:95–106. 10.18865/ed.27.2.95 PubMed DOI PMC

Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. (2021) 22:14. 10.1186/s12910-021-00577-8 PubMed DOI PMC

World Health Organization [WHO]. Big Data and Artificial Intelligence or Achieving Universal Health Coverage: An International Consultation on Ethics: Meeting Report. Geneva: WHO; (2017).

World Health Organization [WHO],. Global Strategy on Digital Health 2020-2025. Geneva: WHO; (2021).

Weissglass DE. Contextual bias, the democratization of healthcare, and medical artificial intelligence in low- and middle-income countries. Bioethics. (2022) 36:201–9. 10.1111/bioe.12927 PubMed DOI

National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Care Services, Board on Global Health, Committee on Improving the Quality of Health Care Globally. Crossing the Global Quality Chasm: Improving Health Care Worldwide. Washington, DC: National Academies Press; (2018). PubMed

Mehta MC, Katz IT, Jha AK. Transforming Global Health with AI. N Engl J Med. (2020) 382:791–3. PubMed

Bram JT, Warwick-Clark B, Obeysekare E, Mehta K. Utilization and monetization of healthcare data in developing countries. Big Data. (2015) 3:59–66. 10.1089/big.2014.0053 PubMed DOI PMC

Guo J, Li B. The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity. (2018) 2:174–81. 10.1089/heq.2018.0037 PubMed DOI PMC

Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. (2020) 395:1579–86. 10.1016/S0140-6736(20)30226-9 PubMed DOI PMC

Gibbons ED. Toward a more equal world: the human rights approach to extending the benefits of artificial intelligence. IEEE Technol Soc Mag. (2021) 40:25–30. 10.1109/MTS.2021.3056295 DOI

Fletcher RR, Nakeshimana A, Olubeko O. Addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health. Front Artif Intell. (2020) 3:561802. 10.3389/frai.2020.561802 PubMed DOI PMC

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. (2018) 169:467–73. PubMed

Peters M, Godfrey C, McInerney P, Munn Z, Tricco A, Khalil H. Chapter 11: Scoping reviews (2020 version). In: Aromataris E, Munn Z. editors. JBI Manual for Evidence Synthesis. Adelaide, AU: JBI; (2020). 10.46658/JBIMES-20-12 DOI

Aria M, Cuccurullo C. Bibliometrix : an R-tool for comprehensive science mapping analysis. J Informetr. (2017) 11:959–75. 10.1016/j.joi.2017.08.007 DOI

World Health Organization [WHO]. Health Topics. (2021). Available online at: https://www.euro.who.int/en/health-topics (accessed November 28, 2021).

World Bank. World Bank Country and Lending Groups – World Bank Data Help Desk. (2021). Available online at: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (accessed November 28, 2021).

Ranganathan P, Aggarwal R. Study designs: part 1 – An overview and classification. Perspect Clin Res. (2018) 9:184–6. PubMed PMC

Feroz A, Jabeen R, Saleem S. Using mobile phones to improve community health workers performance in low-and-middle-income countries. BMC Public Health. (2020) 20:49. 10.1186/s12889-020-8173-3 PubMed DOI PMC

Boden M, Zimmerman L, Azevedo KJ, Ruzek JI, Gala S, Abdel Magid HS, et al. Addressing the mental health impact of COVID-19 through population health. Clin Psychol Rev. (2021) 85:102006. 10.1016/j.cpr.2021.102006 PubMed DOI PMC

Daley CL. The global fight against tuberculosis. Thorac Surg Clin. (2019) 29:19–25. 10.1016/j.thorsurg.2018.09.010 PubMed DOI

Silva DS, Selgelid M. Prevention and treatment of tuberculosis in low- and middle-income countries: ethical challenges. The Oxford Handbook of Public Health Ethics. (2019). Available online at: https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780190245191.001.0001/oxfordhb-9780190245191-e-40 (accessed February 23, 2022). 10.1093/oxfordhb/9780190245191.013.40 DOI

Li Z, Karlsson O, Kim R, Subramanian SV. Distribution of under-5 deaths in the neonatal, postneonatal, and childhood periods: a multicountry analysis in 64 low- and middle-income countries. Int J Equity Health. (2021) 20:109. 10.1186/s12939-021-01449-8 PubMed DOI PMC

Yau M, Timmerman V, Zwarenstein M, Mayers P, Cornick RV, Bateman E, et al. e-PC101: an electronic clinical decision support tool developed in South Africa for primary care in low-income and middle-income countries. BMJ Glob Health. (2019) 3(Suppl. 5):e001093. 10.1136/bmjgh-2018-001093 PubMed DOI PMC

Blobel B, Ruotsalainen P, Oemig F. Why interoperability at data level is not sufficient for enabling pHealth? Stud Health Technol Inform. (2020) 273:3–19. PubMed

Nijiati M, Zhang Z, Abulizi A, Miao H, Tuluhong A, Quan S, et al. Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings. J Xray Sci Technol. (2021) 29:785-96. 10.3233/XST-210894 PubMed DOI

International Standards Organization [ISO]. ISO/IEC 25012:2008. (2008). Available online at: https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/03/57/35736.html (accessed March 20, 2022).

The World Bank. Open Data Essentials | Data. (2021). Available online at: http://opendatatoolkit.worldbank.org/en/essentials.html (accessed March 23, 2022).

International Standards Organization [ISO]. ISO/IEC TR 29119-11:2020. (2020). Available online at: https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/07/90/79016.html (accessed March 20, 2022).

International Standards Organization [ISO]. ISO/TS 21547:2010. (2010). Available online at: https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/04/44/44479.html (accessed March 20, 2022).

International Standards Organization [ISO]. ISO/TS 55010:2019. (2019). Available online at: https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/07/27/72700.html (accessed March 20, 2022).

International Standards Organization [ISO]. ISO/TR 14292:2012. (2012). Available online at: https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/05/45/54568.html (accessed March 20, 2022).

Belle V, Papantonis I. Principles and practice of explainable machine learning. Front Big Data. (2021) 4:688969. 10.3389/fdata.2021.688969 PubMed DOI PMC

International Standards Organization [ISO]. ISO 18308:2011. (2011). Available online at: https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/05/28/52823.html (accessed March 20, 2022).

Taylor L. The ethics of big data as a public good: which public? Whose good? Philos Trans R Soc Math Phys Eng Sci. (2016) 374:20160126. 10.1098/rsta.2016.0126 PubMed DOI PMC