{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T21:29:39Z","timestamp":1779312579777,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence offers transformative potential for global health, yet regulatory disparities threaten equitable adoption. This brief examines whether the World Health Organization\u2019s Prequalification (PQ) framework should extend to AI. We highlight limitations of applying static product paradigms to dynamic systems, outline targeted reforms, and propose a reimagined PQ4AI that balances global technical assurance with local regulatory accountability to foster safe, effective, and equitable deployments.<\/jats:p>","DOI":"10.1038\/s41746-025-02151-7","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T09:04:43Z","timestamp":1765962283000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Do we need prequalification of AI as a medical device to drive equitable adoption"],"prefix":"10.1038","volume":"8","author":[{"given":"Bilal A.","family":"Mateen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernard","family":"Aryeetey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Delese Mimi","family":"Darko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"2151_CR1","doi-asserted-by":"publisher","unstructured":"Busch, F. et al. AI regulation in healthcare around the world: what is the status quo? Preprint at https:\/\/doi.org\/10.1101\/2025.01.25.25321061 (2025).","DOI":"10.1101\/2025.01.25.25321061"},{"key":"2151_CR2","unstructured":"Leow, A., Hu, J. & Hird, T. An overview of WHO Prequalification. Rethink Priorities. Available at: https:\/\/rethinkpriorities.org\/wp-content\/uploads\/2023\/07\/WHO-PQ.pdf. (Accessed on 4th August 2025)."},{"key":"2151_CR3","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1002\/cpt.1680","volume":"107","author":"TF Blaschke","year":"2019","unstructured":"Blaschke, T. F., Lumpkin, M. & Hartman, D. The World Health Organization Prequalification Program and Clinical Pharmacology in 2030. Clin. Pharmacol. Therapeutics 107, 68\u201371 (2019).","journal-title":"Clin. Pharmacol. Therapeutics"},{"key":"2151_CR4","unstructured":"World Health Organization. WHO RPQ impact assessment: Regulation and prequalification Activities. World Health Organization. Available at: https:\/\/cdn.who.int\/media\/docs\/default-source\/medicines\/regulatory-updates\/rpq\/who-rpq-impact-assessment-report_2023.pdf?sfvrsn=3b3a9b8_1. (2023)."},{"key":"2151_CR5","unstructured":"World Health Organisation. List of National Regulatory Authorities (NRAs) operating at maturity level 3 (ML3) and maturity level 4 (ML4). World Health Organization. Available at: https:\/\/cdn.who.int\/media\/docs\/default-source\/medicines\/regulatory-systems\/wla\/list-of-nras-operating-at-ml3-and-ml4.pdf?sfvrsn=ee93064f_23&download=true (2025)."},{"key":"2151_CR6","unstructured":"World Health Organization. WHO Global Benchmarking Tool + Medical Devices (GBT + medical devices) for evaluation of national regulatory systems of medical devices including in-vitro diagnostics. (World Health Organization, Geneva, 2024)."},{"key":"2151_CR7","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1056\/NEJMc2104626","volume":"385","author":"SG Finlayson","year":"2021","unstructured":"Finlayson, S. G. et al. The Clinician and Dataset Shift in Artificial Intelligence. N. Engl. J. Med. 385, 283\u2013286 (2021).","journal-title":"N. Engl. J. Med."},{"key":"2151_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01127-3","volume":"7","author":"L Goetz","year":"2024","unstructured":"Goetz, L., Seedat, N., Vandersluis, R. & Van Der Schaar, M. Generalization\u2014a key challenge for responsible AI in patient-facing clinical applications. NPJ Digital Med. 7, 126 (2024).","journal-title":"NPJ Digital Med."},{"key":"2151_CR9","unstructured":"US Food and Drug Administration. Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions (FDA-2022-D-2628). Food and Drug Administration. (Silver Spring, MD, USA, 2024)."},{"key":"2151_CR10","unstructured":"MHRA. Good machine learning practice for medical device development: Guiding principles (no date). GOV.UK. Available at: https:\/\/www.gov.uk\/government\/publications\/good-machine-learning-practice-for-medical-device-development-guiding-principles\/good-machine-learning-practice-for-medical-device-development-guiding-principles (Accessed: 05 August 2025)."},{"key":"2151_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01542-0","volume":"8","author":"CT Chang","year":"2025","unstructured":"Chang, C. T. et al. Red teaming ChatGPT in medicine to yield real-world insights on model behavior. npj Digital Med. 8, 149 (2025).","journal-title":"npj Digital Med."},{"key":"2151_CR12","unstructured":"Global Digital Health Certification Network (no date) World Health Organization. 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Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study. BMJ 384, e074821 (2024).","journal-title":"BMJ"},{"key":"2151_CR16","doi-asserted-by":"crossref","unstructured":"Mateen, B. A. & Reid, M. J. A. A Letter about \u201cProspective Multisite Validation of AI to Detect Tuberculosis and Chest X-Ray Abnormalities\u201d. NEJM AI 2, (2025).","DOI":"10.1056\/AIp2401190"},{"key":"2151_CR17","doi-asserted-by":"publisher","first-page":"e1003752","DOI":"10.1371\/journal.pmed.1003752","volume":"18","author":"P Macpherson","year":"2021","unstructured":"Macpherson, P. et al. Computer-aided X-ray screening for tuberculosis and HIV testing among adults with cough in Malawi (the PROSPECT study): A randomised trial and cost-effectiveness analysis. 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The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"785"}}