Skip to main content
Log in

Global Health in the Age of AI: Charting a Course for Ethical Implementation and Societal Benefit

  • Published:
Minds and Machines Aims and scope Submit manuscript

Abstract

Artificial Intelligence (AI) presents unprecedented opportunities to transform healthcare worldwide, from improving diagnostic accuracy to expanding access in underserved regions. Despite this potential and growing investment, a significant gap persists between AI’s theoretical promise and its realised benefits in healthcare settings. This article examines the complex barriers impeding AI benefits realization in global health contexts, including ethical uncertainties, data infrastructure limitations, evidence quality concerns, and regulatory ambiguities. We analyze current initiatives addressing these challenges and highlight how technological solutions alone cannot resolve fundamental healthcare inequities. Drawing on the interdisciplinary perspectives and insights presented at the Global Health in the Age of AI Symposium hosted by the Cini Foundation and Yale Digital Ethics Center, we propose five core infrastructure requirements necessary for ethical AI implementation: robust data exchange; epistemic certainty with staff autonomy; actively protected healthcare values; validated outcomes with meaningful accountability; and environmental sustainability. These requirements form the foundation for a systems approach that balances technological advancement with ethical imperatives, contextual adaptability, and global equity considerations. We conclude that the successful integration of AI into healthcare demands coordinated action across sectors and borders, with careful attention to avoiding technological colonialism and ensuring AI serves as a force for health equity rather than widening existing disparities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

Not applicable.

Notes

  1. https://www.cini.it/en/eventi/global-health-in-the-age-of-ai-charting-a-course-for-ethical-implementation-and-societal-benefit/.

  2. These numbers are based on the TOP500 list which updates biannually, these numbers are from November 2024 https://top500.org/lists/top500/2024/11/.

  3. For example, those produced by US Food and Drugs Administration (FDA); UK Medicines and Healthcare products Regulatory Agency (MHRA); China National Medical Products Administration (NMPA); Japan Pharmaceuticals and Medical Devices Agency (PMDA); Australia Therapeutic Goods Administration (TGA); and India Central Drugs Standard Control Organization (CDSCO).

  4. The IMDRF includes regulatory authorities from Australia, Brazil, Canada, China, Europe, Japan, Russia, Singapore, South Korea, the UK, and the US.

  5. This is 73% of the 98% of European member states that responded.

References

  • Abbasian, M., et al. (2024). Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI. Npj Digital Medicine, 7, 1–14.

    Article  Google Scholar 

  • Aboy, M., Minssen, T., & Vayena, E. (2024). Navigating the EU AI Act: Implications for regulated digital medical products. Npj Digital Medicine, 7, 237.

    Article  Google Scholar 

  • Adams, L. et al. (2024). Artificial intelligence in health, health care, and biomedical science: An AI code of conduct principles and commitments discussion draft. National Academy of Medicine https://nam.edu/perspectives/artificial-intelligence-in-health-health-care-and-biomedical-science-an-ai-code-of-conduct-principles-and-commitments-discussion-draft/#:~:text=The%20draft%20AI%20Code%20of,health%20systems%2C%20payers%2C%20patients%2C.

  • Adedinsewo, D. A., et al. (2025). Contextual challenges in implementing artificial intelligence for healthcare in low-resource environments: Insights from the SPEC-AI Nigeria trial. Frontiers in Cardiovascular Medicine, 12, 1516088.

    Article  Google Scholar 

  • Aitken, M., Porteous, C., Creamer, E., & Cunningham-Burley, S. (2018). Who benefits and how? Public expectations of public benefits from data-intensive health research. Big Data & Society, 5, 205395171881672.

    Article  Google Scholar 

  • Alberto, I. R. I., et al. (2023). The impact of commercial health datasets on medical research and health-care algorithms. The Lancet Digital Health, 5, e288–e294.

    Article  Google Scholar 

  • Amugongo, L. M., Bidwell, N. J. & Corrigan, C. C. (2023). Invigorating Ubuntu Ethics in AI for healthcare: Enabling equitable care. (pp. 583–592). https://doi.org/10.1145/3593013.3594024.

  • Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20, 973–989.

    Article  Google Scholar 

  • Andaur Navarro, C. L., et al. (2023). Systematic review finds “Spin” practices and poor reporting standards in studies on machine learning-based prediction models. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2023.03.024

    Article  Google Scholar 

  • Ang, A. (2024). Singapore invests $150M for public health genAI adoption. https://www.healthcareitnews.com/news/asia/singapore-invests-150m-public-health-genai-adoption.

  • Angus, D. C. (2020). Randomized clinical trials of artificial intelligence. JAMA, 323, 1043.

    Article  Google Scholar 

  • Aouragh, M., Gürses, S., Pritchard, H., & Snelting, F. (2020). The extractive infrastructures of contact tracing apps. Journal of Environmental Media, 1, 9.1-9.9.

    Article  Google Scholar 

  • Ayorinde, A., et al. (2024). Health care professionals’ experience of using AI: Systematic review with narrative synthesis. Journal of Medical Internet Research, 26, Article e55766.

    Article  Google Scholar 

  • Bailey, S., Pierides, D., Brisley, A., Weisshaar, C., & Blakeman, T. (2020). Dismembering organisation: The coordination of algorithmic work in healthcare. Current Sociology, 68, 546–571.

    Article  Google Scholar 

  • Barbalho, I. M. P., et al. (2022). Electronic health records in Brazil: Prospects and technological challenges. Frontiers in Public Health, 10, Article 963841.

    Article  Google Scholar 

  • Baron, J., & Spranca, M. (1997). Protected values. Organizational Behavior and Human Decision Processes, 70, 1–16.

    Article  Google Scholar 

  • Beyleveld, D., & Brownsword, R. (2012). Emerging technologies, extreme uncertainty, and the principle of rational precautionary reasoning. Law, Innovation and Technology, 4, 35–65.

    Article  Google Scholar 

  • Bezemer, T., et al. (2019). A human(e) factor in clinical decision support systems. Journal of Medical Internet Research, 21, e11732.

    Article  Google Scholar 

  • Blease, C., et al. (2019). Artificial intelligence and the future of primary care: Exploratory qualitative study of UK general practitioners’ views. Journal of Medical Internet Research, 21, Article e12802.

    Article  Google Scholar 

  • Blease, C. R., Locher, C., Gaab, J., Hägglund, M., & Mandl, K. D. (2024). Generative artificial intelligence in primary care: an online survey of UK general practitioners. BMJ Health Care Inform, 31, e101102.

    Article  Google Scholar 

  • Boag, W., et al. (2024). The algorithm journey map: a tangible approach to implementing AI solutions in healthcare. Npj Digital Medicine, 7, 87.

    Article  Google Scholar 

  • Bouderhem, R. (2024). Shaping the future of AI in healthcare through ethics and governance. Humanit Soc Sci Commun, 11, 416.

    Article  Google Scholar 

  • Bowman, S. (2013). Impact of electronic health record systems on information integrity: Quality and safety implications. Perspectives in Health Information Management, 10, 1c.

    Google Scholar 

  • Burns, P. B., Rohrich, R. J., & Chung, K. C. (2011). The levels of evidence and their role in evidence-based medicine. Plastic and Reconstructive Surgery, 128, 305–310.

    Article  Google Scholar 

  • Busch, F., et al. (2025). AI regulation in healthcare around the world: What is the status quo? Preprint at. https://doi.org/10.1101/2025.01.25.25321061

    Article  Google Scholar 

  • Canada, H. (2024). Pan-Canadian AI for health (AI4H) guiding principles. https://www.canada.ca/en/health-canada/corporate/transparency/health-agreements/pan-canadian-ai-guiding-principles.html.

  • Chagnon, C., & Hagalani-Albov, S. (2023). Data extractivism: Social pollution and real-world costs. In J. Lubacha, B. Mäihäniemi, & R. Wisła (Eds.), The European digital economy: Drivers of digital transition and economic recovery. Routledge.

    Google Scholar 

  • CHAI. (2025). Responsible AI guide. CHAI—Coalition for Health AI https://chai.org/responsible-ai-guide/

  • Chekroud, A. M., et al. (2024). Illusory generalizability of clinical prediction models. Science, 383, 164–167.

    Article  Google Scholar 

  • Chinn, S., Hasell, A., & Hiaeshutter-Rice, D. (2023). Mapping digital wellness content: Implications for health, science, and political communication research. Journal of Quantitative Description: Digital Media, 3, 1–56.

    Google Scholar 

  • Cohen, I. G., Evgeniou, T., Gerke, S., & Minssen, T. (2020). The European artificial intelligence strategy: Implications and challenges for digital health. The Lancet Digital Health, 2, e376–e379.

    Article  Google Scholar 

  • Coiera, E. (2019). Assessing technology success and failure using information value chain theory. In Studies in health technology and informatics IOS Press. https://doi.org/10.3233/SHTI190109.

  • Collective Action for Responsible AI in Health. (2024). vol. 10 https://www.oecd.org/en/publications/collective-action-for-responsible-ai-in-health_f2050177-en.html.

  • CzechTrade. (2024). Two-Thirds of Czech Hospitals Utilise Artificial Intelligence. https://www.czechtradeoffices.com/au/news/two-thirds-of-czech-hospitals-utilise-artificial-intelligence.

  • Dahal, D. P., & Sharma, P. (2023). Exploring the challenges and opportunities of implementing artificial intelligence in healthcare settings in Nepal: A literature review. Kathmandu University of Medical Journal (KUMJ), 21, 436–443.

    Google Scholar 

  • Detmer, D. E. (2003). Building the national health information infrastructure for personal health, health care services, public health, and research. BMC Medical Informatics and Decision Making, 3, 1.

    Article  Google Scholar 

  • Dijkstra, P., Greenhalgh, T., Mekki, Y. M., & Morley, J. (2025). How to read a paper involving artificial intelligence (AI). Bmjmed, 4, e001394.

    Article  Google Scholar 

  • Dixon, B. E., & Grannis, S. J. (2020). Information infrastructure to support public health. In J. A. Magnuson & B. E. Dixon (Eds.), Public health informatics and information systems (pp. 83–104). Springer International Publishing.

    Chapter  Google Scholar 

  • Dobbe, R., & Wolters, A. (2024). Toward sociotechnical AI: mapping vulnerabilities for machine learning in context. Minds & Machines, 34, 12.

    Article  Google Scholar 

  • Ethics and Governance of Artificial Intelligence for Health: WHO Guidance. (2021). Executive summary. World Health Organization, Geneva.

  • FDA. (2024). Artificial intelligence and machine learning (AI/ML)-Enabled Medical Devices. FDA.

  • Ferlito, B., Segers, S., De Proost, M., & Mertes, H. (2024). Responsibility gap(s) due to the introduction of AI in healthcare: An Ubuntu-inspired approach. Science and Engineering Ethics, 30, 34.

    Article  Google Scholar 

  • Fiske, A., Prainsack, B., & Buyx, A. (2019). Data work: Meaning-making in the era of data-rich medicine. Journal of Medical Internet Research, 21, Article e11672.

    Article  Google Scholar 

  • Fiske, A. Climate change and health: The next challenge of ethical AI. Accepted with Lancet Global Health. Lancet Global Health (Forthcoming).

  • Floridi, L. (2019). Translating principles into practices of digital ethics: Five risks of being unethical. Philosophy & Technology, 32, 185–193.

    Article  Google Scholar 

  • Floridi, L. (2020). AI and its new winter: From myths to realities. Philosophy & Technology, 33, 1–3.

    Article  Google Scholar 

  • Floridi, L. (2024). Why the AI hype is another tech bubble. Philosophy & Technology, 37, 128.

    Article  Google Scholar 

  • Floridi, L. (2025). AI as agency without intelligence: On artificial intelligence as a new form of artificial agency and the multiple realisability of agency thesis. Philosophy & Technology, 38, 30.

    Article  Google Scholar 

  • Gadotti, A., Rocher, L., Houssiau, F., Creţu, A.-M., & De Montjoye, Y.-A. (2024). Anonymization: The imperfect science of using data while preserving privacy. Science Advance, 10, 7053.

    Google Scholar 

  • Gerke, S., Minssen, T. & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare (pp. 295–336). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00012-5.

  • Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3, e745–e750.

    Article  Google Scholar 

  • Goldacre, B. & Morley, J. (2022). Better, broader, safer: Using health data for research and analysis. GOV.UK https://www.gov.uk/government/publications/better-broader-safer-using-health-data-for-research-and-analysis.

  • Greenhalgh, T., et al. (2017). Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19, Article e367.

    Article  Google Scholar 

  • Harrer, S. (2023). Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. eBioMedicine, 90, 104512.

    Article  Google Scholar 

  • Ibrahim, H., Liu, X., Zariffa, N., Morris, A. D., & Denniston, A. K. (2021). Health data poverty: An assailable barrier to equitable digital health care. The Lancet Digital Health, 3, e260–e265.

    Article  Google Scholar 

  • Jankovic, I., & Chen, J. H. (2020). Clinical decision support and implications for the clinician burnout crisis. Yearbook of Medical Informatics, 29, 145–154.

    Article  Google Scholar 

  • Joshi, S., et al. (2025). AI as an intervention: Improving clinical outcomes relies on a causal approach to AI development and validation. Journal of the American Medical Informatics Association, 32, 589–594.

    Article  Google Scholar 

  • Karpathakis, K., Morley, J., & Floridi, L. (2024). A justifiable investment in AI for healthcare: aligning ambition with reality. Minds & Machines, 34, 38.

    Article  Google Scholar 

  • Kerasidou, A. (2020). Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare. Bulletin of the World Health Organization, 98, 245–250.

    Article  Google Scholar 

  • Kerr, D. (2024). How Memphis became a battleground over Elon Musk’s xAI supercomputer. KERA News https://www.keranews.org/2024-09-11/how-memphis-became-a-battleground-over-elon-musks-xai-supercomputer.

  • Kim, H.-E., et al. (2020). Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study. The Lancet Digital Health, 2, e138–e148.

    Article  Google Scholar 

  • Kolbinger, F. R., Veldhuizen, G. P., Zhu, J., Truhn, D., & Kather, J. N. (2024). Reporting guidelines in medical artificial intelligence: A systematic review and meta-analysis. Communication & Medicine, 4, 71.

    Article  Google Scholar 

  • Kostick-Quenet, K. M. (2025). A caution against customized AI in healthcare. Npj Digital Medicine, 8, 13.

    Article  Google Scholar 

  • Kumar, D., Ingole, A., & Choudhari, S. G. (2023). Towards ideal health ecosystem with artificial intelligence-driven medical services in India: An overview. Cureus. https://doi.org/10.7759/cureus.48482

    Article  Google Scholar 

  • Lee, S.S.-J. (2021). Obligations of the “Gift”: Reciprocity and responsibility in precision medicine. The American Journal of Bioethics, 21, 57–66.

    Article  Google Scholar 

  • Lekadir, K., et al. (2025). FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. https://doi.org/10.1136/bmj-2024-081554

    Article  Google Scholar 

  • Lock, M. M. & Nguyen, V.-K. (2018). An anthropology of biomedicine. Wiley Blackwell.

  • Lu, A. D., et al. (2021). Implementation strategies for frontline healthcare professionals: People, process mapping, and problem solving. Journal of General Internal Medicine, 36, 506–510.

    Article  Google Scholar 

  • Ludwig, F. et al. (2007). Climate change impacts on developing countries—EU accountability. https://library.wur.nl/WebQuery/wurpubs/359693.

  • Lyell, D., Lustig, A., Denyer, K., Vedantam, S., & Magrabi, F. (2024). Using clinical simulation to evaluate AI-enabled decision support. In J. Bichel-Findlay, P. Otero, P. Scott, & E. Huesing (Eds.), Studies in health technology and informatics. IOS Press.

    Google Scholar 

  • Mashima, D. & Ahamad, M. (2012). Enabling robust information accountability in E-healthcare systems.

  • Maslej, N. et al. (2023). Artificial intelligence index report 2023. Preprint at https://doi.org/10.48550/ARXIV.2310.03715

  • McCarthy, J., Minsky, M., Rochester, N. & Shannon, C. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf.

  • McCoy, L. G., et al. (2025). Building health systems capable of leveraging AI: Applying Paul Farmer’s 5S framework for equitable global health. BMC Global Public Health, 3, 39.

    Article  Google Scholar 

  • McCradden, M. D., et al. (2025). CANAIRI: The collaboration for translational artificial intelligence trials in healthcare. Nature Medicine, 31, 9–11.

    Article  Google Scholar 

  • McCradden, M. D., Joshi, S., Mazwi, M., & Anderson, J. A. (2020a). Ethical limitations of algorithmic fairness solutions in health care machine learning. The Lancet Digital Health, 2, e221–e223.

    Article  Google Scholar 

  • McCradden, M. D., Stephenson, E. A., & Anderson, J. A. (2020b). Clinical research underlies ethical integration of healthcare artificial intelligence. Nature Medicine, 26, 1325–1326.

    Article  Google Scholar 

  • McLennan, S., Fiske, A., & Celi, L. A. (2024). Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine. BMJ Health Care Inform, 31, Article e101052.

    Article  Google Scholar 

  • Molnár-Gábor, F. (2020). Artificial intelligence in healthcare: Doctors, patients and liabilities. In T. Wischmeyer & T. Rademacher (Eds.), Regulating artificial intelligence (pp. 337–360). Springer International Publishing.

    Chapter  Google Scholar 

  • Morley, J. & Floridi, L. (2024). The ethics of AI in health care: An updated mapping review. Preprint at https://doi.org/10.2139/ssrn.4987317.

  • Morley, J., et al. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, Article 113172.

    Article  Google Scholar 

  • Moss, S. (2023). Microsoft’s water consumption jumps 34 percent amid AI boom. Data Center Dynamics https://www.datacenterdynamics.com/en/news/microsofts-water-consumption-jumps-34-percent-amid-ai-boom/.

  • Näher, A.-F., et al. (2023). Secondary data for global health digitalisation. The Lancet Digital Health, 5, e93–e101.

    Article  Google Scholar 

  • Nsoesie, E. O. (2018). Evaluating artificial intelligence applications in clinical settings. JAMA Network Open, 1, Article e182658.

    Article  Google Scholar 

  • Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine learning, and clinical medicine. New England Journal of Medicine, 375, 1216–1219.

    Article  Google Scholar 

  • OECD. (2024). Artificial Intelligence and the Health Workforce: Perspectives from Medical Associations on AI in Health. vol. 28 https://www.oecd.org/en/publications/artificial-intelligence-and-the-health-workforce_9a31d8af-en.html.

  • Ogolodom, M. P., et al. (2023). Knowledge and perception of healthcare workers towards the adoption of artificial intelligence in healthcare service delivery in Nigeria. AG Salud, 1, 16.

    Article  Google Scholar 

  • Olakotan, O. O., & Mohd Yusof, M. (2021). The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review. Health Informatics Journal, 27, 14604582211007536.

    Article  Google Scholar 

  • Ong, J. C. L. et al. (2025). Regulatory science innovation for generative AI and large language models in health and medicine: A global call for action. Preprint at https://doi.org/10.48550/arXiv.2502.07794.

  • Onitiu, D., Wachter, S., & Mittelstadt, B. (2024). How AI challenges the medical device regulation: patient safety, benefits, and intended uses. Journal of Law and the Biosciences. https://doi.org/10.1093/jlb/lsae007

    Article  Google Scholar 

  • Panch, T., Mattie, H., & Atun, R. (2019b). Artificial intelligence and algorithmic bias: Implications for health systems. Journal of Global Health, 9, Article 010318.

    Article  Google Scholar 

  • Panch, T., Mattie, H., & Celi, L. A. (2019a). The, “inconvenient truth” about AI in healthcare. Npj Digital Medicine, 2, 77.

    Article  Google Scholar 

  • Parry, C. & Aneja, U. (2023). Artificial intelligence for healthcare: Insights from India | 3. AI in healthcare in India: applications, challenges and risks. https://www.chathamhouse.org/2020/07/artificial-intelligence-healthcare-insights-india/3-ai-healthcare-india-applications.

  • Praveen, S. P., et al. (2022). A robust framework for handling health care information based on machine learning and big data engineering techniques. International Journal of Healthcare Management. https://doi.org/10.1080/20479700.2022.2157071

    Article  Google Scholar 

  • Price, W. N., II., Gerke, S., & Cohen, I. G. (2019). Potential liability for physicians using artificial intelligence. JAMA, 322, 1765–1766.

    Article  Google Scholar 

  • Pucher, G., et al. (2025). Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis. eBioMedicine, 111, 105526.

    Article  Google Scholar 

  • Reddy, S. (2025). Global harmonization of artificial intelligence-enabled software as a medical device regulation: addressing challenges and unifying standards. Mayo Clinic Proceedings: Digital Health, 3, Article 100191.

    Google Scholar 

  • Regazzoni, J. (2024). Health, Latin America, and the promise of artificial intelligence. Think Global Health https://www.thinkglobalhealth.org/article/health-latin-america-and-promise-artificial-intelligence.

  • Sarkar, U., & Samal, L. (2020). How effective are clinical decision support systems? BMJ. https://doi.org/10.1136/bmj.m3499

    Article  Google Scholar 

  • SCORE for Health Data Technical Package: Global Report on Health Data Systems and Capacity 2020. (2021). World Health Organization, Geneva.

  • Seastedt, K. P., et al. (2022). Global healthcare fairness: We should be sharing more, not less, data. PLOS Digit Health, 1, Article e0000102.

    Article  Google Scholar 

  • Shinners, L., Aggar, C., Stephens, A., & Grace, S. (2023). Healthcare professionals’ experiences and perceptions of artificial intelligence in regional and rural health districts in Australia. Australian Journal of Rural Health, 31, 1203–1213.

    Article  Google Scholar 

  • Shipton, L., & Vitale, L. (2024). Artificial intelligence and the politics of avoidance in global health. Social Science and Medicine, 359, 117274.

    Article  Google Scholar 

  • Sittig, D. F., & Singh, H. (2010). A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality and Safety in Health Care, 19, i68–i74.

    Article  Google Scholar 

  • The OpenSAFELY Collaborative et al. (2024). Consistency, completeness and external validity of ethnicity recording in NHS primary care records: a cohort study in 25 million patients’ records at source using OpenSAFELY. BMC Medicine 22, 288

  • Tochibayashi, N. & Kutty, N. (2023). Three AI tools revolutionising healthcare in Japan. World Economic Forum https://www.weforum.org/stories/2023/12/three-ai-tools-setting-the-stage-for-a-tech-revolution-by-japans-entrepreneurial-doctors/.

  • Van De Sande, D., et al. (2024). To warrant clinical adoption AI models require a multi-faceted implementation evaluation. Npj Digital Medicine, 7, 58.

    Article  Google Scholar 

  • Wang, C., Zhang, J., Lassi, N., & Zhang, X. (2022). Privacy protection in using artificial intelligence for healthcare: Chinese regulation in comparative perspective. Healthcare, 10, 1878.

    Article  Google Scholar 

  • Warraich, H. J., Tazbaz, T., & Califf, R. M. (2025). FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA, 333, 241.

    Article  Google Scholar 

  • Weissman, G. E., Mankowitz, T., & Kanter, G. P. (2025). Unregulated large language models produce medical device-like output. Npj Digital Medicine, 8, 148.

    Article  Google Scholar 

  • WHO, World Bank & IRENA & SEforALL. (2023). Energizing health: Accelerating electricity access in health-care facilities: Executive summary. https://www.who.int/publications/i/item/9789240066984.

  • WHO. (2025b) Health workforce. https://www.who.int/health-topics/health-workforce.

  • WHO. (2025a). Medical devices. https://www.who.int/health-topics/medical-devices.

  • Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Natural Biomedicine Engineering, 2, 719–731.

    Article  Google Scholar 

  • Zhang, J., et al. (2023). Mapping and evaluating national data flows: transparency, privacy, and guiding infrastructural transformation. The Lancet Digital Health, 5, e737.

    Article  Google Scholar 

Download references

Acknowledgements

This research is the outcome of the three-day symposium, entitled “Global Health in the Age of AI”, organised by the Fondazione Giorgio Cini, in collaboration with Yale’s Digital Ethics Center, on 7-9 November 2024, in the Island of San Giorgio Maggiore, Venice. The authors are very grateful for the support they received from the Foundation

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jessica Morley.

Ethics declarations

Conflict of interest

They declare no conflict of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 94 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Morley, J., Hine, E., Roberts, H. et al. Global Health in the Age of AI: Charting a Course for Ethical Implementation and Societal Benefit. Minds & Machines 35, 31 (2025). https://doi.org/10.1007/s11023-025-09730-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11023-025-09730-3

Keywords

Profiles

  1. Jessica Morley
  2. Federica Mandreoli
  3. Hannah van Kolfschooten