{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:59:43Z","timestamp":1790621983043,"version":"4.1.0"},"reference-count":22,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR AI"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n AI systems are increasingly deployed across National Health Service (NHS) services, yet safety and implementation challenges may only become apparent after clinical go-live. Existing governance and implementation frameworks provide valuable high-level guidance, but health care provider organizations still require practical, auditable tools to support preimplementation decision-making.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to develop a deployment-derived AI readiness checklist and assess its early feasibility, face validity, and content validity within the originating NHS Trust context.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted a pragmatic checklist development study with retrospective structured application in a UK NHS district general hospital (George Eliot Hospital NHS Trust). The SID & ADE AI Pre-Implementation Checklist was developed from empirical learning across trust AI deployment activity, primarily an AI fracture detection system and an AI-supported prostate magnetic resonance imaging pathway. Evidence sources included a clinico-AI discordance study, the Quality, Service Improvement and Redesign program using plan-do-study-act cycles, and governance artifacts from AI deployment activities. Safety, governance, operational, workforce, information governance, procurement, and monitoring gaps were translated into auditable preimplementation requirements. The checklist was retrospectively applied to the same deployments from which it was derived to assess readiness completeness and demonstrate face and content validity within the originating context. This design was not intended to establish independent construct or predictive validity.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The checklist comprises 8 domains: use-case definition; clinical safety and accountability; local validation and performance; workforce readiness and human factors; operational and technical integration; information governance and ethics; procurement, liability, and financial risk; and monitoring, evaluation, and stop rules. Retrospective application demonstrated variability in readiness completeness across domains, with recurrent gaps in workforce readiness, local validation, and monitoring. The process highlighted areas where structured pre\u2013go-live deliberation may have prompted earlier remediation and clearer governance action.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The SID & ADE AI Pre-Implementation Checklist translates real-world AI deployment learning into a practical preimplementation deliberation tool. Current evidence supports face and content validity within the originating trust context, but independent prospective validation is required before claims of predictive validity, generalizability, or quantitative go-live thresholds can be made.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/93900","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T11:40:08Z","timestamp":1781264408000},"page":"e93900-e93900","source":"Crossref","is-referenced-by-count":0,"title":["Translating Real-World Safety and Implementation Gaps Into a Deployment-Derived AI Readiness Preimplementation Checklist for NHS Health Care Providers: Checklist Development Study"],"prefix":"10.2196","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0612-5866","authenticated-orcid":false,"given":"Adesina","family":"Adesuyi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6954-7846","authenticated-orcid":false,"given":"Sid","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7336-9341","authenticated-orcid":false,"given":"Vinod","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9566-2626","authenticated-orcid":false,"given":"Ponnusamy","family":"Saravanan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"issue":"1","key":"R1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1093\/bmb\/ldab016","article-title":"The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare","volume":"139","author":"Aung","journal-title":"Br Med Bull"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","journal-title":"Nat Med"},{"issue":"10","key":"R3","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.46454","article-title":"A systematic review of the barriers to the implementation of artificial intelligence in healthcare","volume":"15","author":"Ahmed","journal-title":"Cureus"},{"issue":"2","key":"R4","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1136\/bmjinnov-2019-000359","article-title":"Bridging the implementation gap of machine learning in healthcare","volume":"6","author":"Seneviratne","journal-title":"BMJ Innov"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1136\/amiajnl-2011-000089","article-title":"Automation bias: a systematic review of frequency, effect mediators, and mitigators","volume":"19","author":"Goddard","journal-title":"J Am Med Inform Assoc"},{"issue":"2","key":"R6","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1093\/jamia\/ocw105","article-title":"Automation bias and verification complexity: a systematic review","volume":"24","author":"Lyell","journal-title":"J Am Med Inform Assoc"},{"issue":"3","key":"R7","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1056\/NEJMc2104626","article-title":"The clinician and dataset shift in artificial intelligence","volume":"385","author":"Finlayson","journal-title":"N Engl J Med"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00611-y","article-title":"Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare","volume":"5","author":"Feng","journal-title":"NPJ Digit Med"},{"issue":"8","key":"R9","doi-asserted-by":"publisher","DOI":"10.1001\/jamahealthforum.2025.2724","article-title":"Understanding model drift and its impact on health care policy","volume":"6","author":"Wong","journal-title":"JAMA Health Forum"},{"key":"R10","unstructured":"Ethics and governance of AI for health. 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