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Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?
  1. Brian Wahl1,
  2. Aline Cossy-Gantner2,
  3. Stefan Germann2,
  4. Nina R Schwalbe1
  1. 1 Spark Street Consulting, New York City, New York, USA
  2. 2 Fondation Botnar, Basel, Switzerland
  1. Correspondence to Dr. Brian Wahl; bpwahl{at}


The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. We provide a general overview of AI and how it can be used to improve health outcomes in resource-poor settings. We also describe some of the current ethical debates around patient safety and privacy. Despite current challenges, AI holds tremendous promise for transforming the provision of healthcare services in resource-poor settings. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate realising of the full potential of AI for improving global health.

  • artificial intelligence
  • primary health
  • low- and middle-income countries

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  • Handling editor Seye Abimbola

  • Contributors BW and NRS designed and conducted the analysis. They also prepared the first draft of the manuscript. SG and AC-G conceptualised the project and provided substantial input to the analysis and edits to the manuscript.

  • Funding Fondation Botnar.

  • Competing interests None declared.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement No additional data are available.