{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:58:35Z","timestamp":1782842315186,"version":"3.54.5"},"reference-count":54,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR AI"],"abstract":"\n Background<\/jats:title>\n The COVID-19 pandemic drove investment and research into medical imaging platforms to provide data to create artificial intelligence (AI) algorithms for the management of patients with COVID-19. Building on the success of England\u2019s National COVID-19 Chest Imaging Database, the national digital policy body (NHSX) sought to create a generalized national medical imaging platform for the development, validation, and deployment of algorithms.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to understand international use cases of medical imaging platforms for the development and implementation of algorithms to inform the creation of England\u2019s national imaging platform.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n The National Health Service (NHS) AI Lab Policy and Strategy Team adopted a multiphased approach: (1) identification and prioritization of national AI imaging platforms; (2) Political, Economic, Social, Technological, Legal, and Environmental (PESTLE) factor analysis deep dive into national AI imaging platforms; (3) semistructured interviews with key stakeholders; (4) workshop on emerging themes and insights with the internal NHSX team; and (5) formulation of policy recommendations.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n International use cases of national AI imaging platforms (n=7) were prioritized for PESTLE factor analysis. Stakeholders (n=13) from the international use cases were interviewed. Themes (n=8) from the semistructured interviews, including interview quotes, were analyzed with workshop participants (n=5). The outputs of the deep dives, interviews, and workshop were synthesized thematically into 8 categories with 17 subcategories. On the basis of the insights from the international use cases, policy recommendations (n=12) were developed to support the NHS AI Lab in the design and development of the English national medical imaging platform.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The creation of AI algorithms supporting technology and infrastructure such as platforms often occurs in isolation within countries, let alone between countries. This novel policy research project sought to bridge the gap by learning from the challenges, successes, and experience of England\u2019s international counterparts. Policy recommendations based on international learnings focused on the demonstrable benefits of the platform to secure sustainable funding, validation of algorithms and infrastructure to support in situ deployment, and creating wraparound tools for nontechnical participants such as clinicians to engage with algorithm creation. As health care organizations increasingly adopt technological solutions, policy makers have a responsibility to ensure that initiatives are informed by learnings from both national and international initiatives as well as disseminating the outcomes of their work.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/51168","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T09:15:15Z","timestamp":1704359715000},"page":"e51168","source":"Crossref","is-referenced-by-count":5,"title":["Learning From International Comparators of National Medical Imaging Initiatives for AI Development: Multiphase Qualitative Study"],"prefix":"10.2196","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3079-6268","authenticated-orcid":false,"given":"Kassandra","family":"Karpathakis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9523-439X","authenticated-orcid":false,"given":"Emma","family":"Pencheon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6168-327X","authenticated-orcid":false,"given":"Dominic","family":"Cushnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"ref1","unstructured":"AICan 2020 CIFAR Pan-Canadian AI strategy impact reportCanadian Institute for Advanced 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