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. 2022 Oct 28;5(1):162.
doi: 10.1038/s41746-022-00700-y.

Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review

Tadeusz Ciecierski-Holmes  1   2 , Ritvij Singh  3 , Miriam Axt  4 , Stephan Brenner  4 , Sandra Barteit  4
Affiliations

Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review

Tadeusz Ciecierski-Holmes et al. NPJ Digit Med. .

Abstract

In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health and APA PsycInfo databases, and grey literature from a Google Scholar search. We included studies that reported a quantitative and/or qualitative evaluation of a real-world application of AI in an LMIC health context. A total of 10 references evaluating the application of AI in an LMIC were included. Applications varied widely, including: clinical decision support systems, treatment planning and triage assistants and health chatbots. Only half of the papers reported which algorithms and datasets were used in order to train the AI. A number of challenges of using AI tools were reported, including issues with reliability, mixed impacts on workflows, poor user friendliness and lack of adeptness with local contexts. Many barriers exists that prevent the successful development and adoption of well-performing, context-specific AI tools, such as limited data availability, trust and evidence of cost-effectiveness in LMICs. Additional evaluations of the use of AI in healthcare in LMICs are needed in order to identify their effectiveness and reliability in real-world settings and to generate understanding for best practices for future implementations.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. PRISMA Flowchart of studies included in the systematic review.
Flowchart of study identification, exclusion based on titles and abstracts, and inclusion in the final review after assessing full texts.
See this image and copyright information in PMC

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