Building health systems capable of leveraging AI: applying Paul Farmer's 5S framework for equitable global health

Liam G McCoy^{1

2}, Azra Bihorac³, Leo Anthony Celi¹, Matthew Elmore⁴, Divya Kewalramani⁵, Teddy Kwaga⁶, Nicole Martinez-Martin⁷, Renata Prôa⁸, Joel Schamroth⁹, Jonathan D Shaffer¹⁰, Alaa Youssef¹¹, Amelia Fiske¹²

Affiliations

¹ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
² Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
³ Department of Surgery and Anesthesiology, University Of Florida College of Medicine, Gainesville, FL, USA.
⁴ Duke Health AI Evaluation & Governance, Duke University School of Medicine, Durham, NC, USA.
⁵ Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
⁶ Department of Ophthalmology, Mbarara University of Science and Technology, Mbarara, Uganda.
⁷ Center for Biomedical Ethics, Stanford School of Medicine, Stanford, CA, USA.
⁸ Department of Global Health and Population, Harvard School of Public Health, Boston, MA, USA.
⁹ Faculty of Population Health Sciences, University College London, London, UK.
¹⁰ Department of Sociology, University of Vermont, Burlington, VT, USA.
¹¹ Department of Radiology, Stanford School of Medicine, Stanford, CA, USA.
¹² Institute of History and Ethics in Medicine, Department of Preclinical Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany. a.fiske@tum.de.

PMID: 40312417
PMCID: PMC12046908
DOI: 10.1186/s44263-025-00158-6

Review

Building health systems capable of leveraging AI: applying Paul Farmer's 5S framework for equitable global health

Liam G McCoy et al. BMC Glob Public Health. 2025.

. 2025 May 2;3(1):39.

doi: 10.1186/s44263-025-00158-6.

Authors

Affiliations

¹ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
² Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
³ Department of Surgery and Anesthesiology, University Of Florida College of Medicine, Gainesville, FL, USA.
⁴ Duke Health AI Evaluation & Governance, Duke University School of Medicine, Durham, NC, USA.
⁵ Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
⁶ Department of Ophthalmology, Mbarara University of Science and Technology, Mbarara, Uganda.
⁷ Center for Biomedical Ethics, Stanford School of Medicine, Stanford, CA, USA.
⁸ Department of Global Health and Population, Harvard School of Public Health, Boston, MA, USA.
⁹ Faculty of Population Health Sciences, University College London, London, UK.
¹⁰ Department of Sociology, University of Vermont, Burlington, VT, USA.
¹¹ Department of Radiology, Stanford School of Medicine, Stanford, CA, USA.
¹² Institute of History and Ethics in Medicine, Department of Preclinical Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany. a.fiske@tum.de.

PMID: 40312417
PMCID: PMC12046908
DOI: 10.1186/s44263-025-00158-6

Abstract

The development of artificial intelligence (AI) applications in healthcare is often positioned as a solution to the greatest challenges facing global health. Advocates propose that AI can bridge gaps in care delivery and access, improving healthcare quality and reducing inequity, including in resource-constrained settings. A broad base of critical scholarship has highlighted important issues with healthcare AI, including algorithmic bias and inequitable and inaccurate model outputs. While such criticisms are valid, there exists a much more fundamental challenge that is often overlooked in global health policy debates: the dangerous mismatch between AI's imagined benefits and the material realities of healthcare systems globally. AI cannot be deployed effectively or ethically in contexts lacking sufficient social and material infrastructure and resources to provide effective healthcare services. Continued investments in AI within unprepared, under-resourced contexts risk misallocating resources and potentially causing more harm than good. The article concludes by providing concrete questions to assess AI systemic capacity and socio-technical readiness in global health.

Keywords: Artificial intelligence; Equity; Global health; Health; Health systems; Paul Farmer.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: LAC declares consulting fees from Philips for evaluation of hemodynamic monitoring product; payment/honoraria from Stanford University (visiting professor), University of California San Francisco (visiting professor), University of Toronto (visiting professor), and Taipei Medical University (remote speaker); support for attending meetings and/or travel from Australia New Zealand College of Intensive Care Medicine, University of Bergen, University Medical Center Amsterdam, Académie Nationale de Médecine (France), and the Doris Duke Foundation; leadership/fiduciary roles with PLOS Digital Health (Editor-in-Chief) and The Lancet Digital Health (International Advisory Board member); and receives compute credits for Oracle. JS is also an employee of GSK (a private company) and holds shares in the GSK group of companies. The remaining authors declare no competing interests.

References

1. Novartis Foundation. 2020. Lower-income countries could soon leapfrog high-income countries with AI-enabled health technologies, Novartis Foundation and Microsoft backed report says. Available from: https://www.novartisfoundation.org/news/media-release/lower-income-count.... Cited 2025 Mar 20.
1. Fan Q, Zehnwei Qiang C. World Bank Blogs. 2024. Tipping the scales: AI’s dual impact on developing nations. Available from: https://blogs.worldbank.org/en/digital-development/tipping-the-scales--a.... Cited 2025 Mar 20.
1. Intel AI, Forbes Insights. Forbes. 2019. Welcome from forbes to a special exploration of AI issue 6. Available from: https://www.forbes.com/sites/insights-intelai/2019/05/22/welcome-from-fo.... Cited 2025 Mar 20.
1. Muldoon J, Wu BA. Artificial intelligence in the colonial matrix of power. Philos Technol. 2023;36(4):80.
1. Muldoon J, Graham M, Cant C. Feeding the machine: the hidden human labour powering AI. Main. Edinburgh: Canongate Books; 2024. p. 288.

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Building health systems capable of leveraging AI: applying Paul Farmer's 5S framework for equitable global health

Affiliations

Building health systems capable of leveraging AI: applying Paul Farmer's 5S framework for equitable global health

Authors

Affiliations

Abstract

Conflict of interest statement

References

Publication types

Grants and funding

LinkOut - more resources

Full Text Sources