eprintid: 10156807 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/15/68/07 datestamp: 2022-10-06 15:12:09 lastmod: 2022-10-06 15:12:09 status_changed: 2022-10-06 15:12:09 type: article metadata_visibility: show sword_depositor: 699 creators_name: Zhang, Joe creators_name: Budhdeo, Sanjay creators_name: William, Wasswa creators_name: Cerrato, Paul creators_name: Shuaib, Haris creators_name: Sood, Harpreet creators_name: Ashrafian, Hutan creators_name: Halamka, John creators_name: Teo, James T title: Moving towards vertically integrated artificial intelligence development ispublished: pub divisions: C07 divisions: F84 divisions: B02 divisions: UCL divisions: D07 note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. abstract: Substantial interest and investment in clinical artificial intelligence (AI) research has not resulted in widespread translation to deployed AI solutions. Current attention has focused on bias and explainability in AI algorithm development, external validity and model generalisability, and lack of equity and representation in existing data. While of great importance, these considerations also reflect a model-centric approach seen in published clinical AI research, which focuses on optimising architecture and performance of an AI model on best available datasets. However, even robustly built models using state-of-the-art algorithms may fail once tested in realistic environments due to unpredictability of real-world conditions, out-of-dataset scenarios, characteristics of deployment infrastructure, and lack of added value to clinical workflows relative to cost and potential clinical risks. In this perspective, we define a vertically integrated approach to AI development that incorporates early, cross-disciplinary, consideration of impact evaluation, data lifecycles, and AI production, and explore its implementation in two contrasting AI development pipelines: a scalable "AI factory" (Mayo Clinic, Rochester, United States), and an end-to-end cervical cancer screening platform for resource poor settings (Paps AI, Mbarara, Uganda). We provide practical recommendations for implementers, and discuss future challenges and novel approaches (including a decentralised federated architecture being developed in the NHS (AI4VBH, London, UK)). Growth in global clinical AI research continues unabated, and introduction of vertically integrated teams and development practices can increase the translational potential of future clinical AI projects. date: 2022-09-15 date_type: published publisher: NATURE PORTFOLIO official_url: https://doi.org/10.1038/s41746-022-00690-x oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1977983 doi: 10.1038/s41746-022-00690-x medium: Electronic pii: 10.1038/s41746-022-00690-x lyricists_name: Budhdeo, Sanjay lyricists_id: SBUDH52 actors_name: Budhdeo, Sanjay actors_id: SBUDH52 actors_role: owner funding_acknowledgements: 203928/Z/16/Z [Wellcome Trust]; 566701 [Wellcome Trust]; [National Institute for Health Research (NIHR) Biomedical Research Centre based at Imperial College NHS Trust and Imperial College London] full_text_status: public publication: npj Digital Medicine volume: 5 article_number: 143 pages: 9 event_location: England citation: Zhang, Joe; Budhdeo, Sanjay; William, Wasswa; Cerrato, Paul; Shuaib, Haris; Sood, Harpreet; Ashrafian, Hutan; ... Teo, James T; + view all <#> Zhang, Joe; Budhdeo, Sanjay; William, Wasswa; Cerrato, Paul; Shuaib, Haris; Sood, Harpreet; Ashrafian, Hutan; Halamka, John; Teo, James T; - view fewer <#> (2022) Moving towards vertically integrated artificial intelligence development. npj Digital Medicine , 5 , Article 143. 10.1038/s41746-022-00690-x <https://doi.org/10.1038/s41746-022-00690-x>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10156807/1/Moving%20towards%20vertically%20integrated%20artificial%20intelligence%20development.pdf