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Moving towards vertically integrated artificial intelligence development

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Title: Moving towards vertically integrated artificial intelligence development
Authors: Zhang, J
Budhdeo, S
William, W
Cerrato, P
Shuaib, H
Sood, H
Ashrafian, H
Halamka, J
Teo, J
Item Type: Journal Article
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.
Issue Date: 15-Sep-2022
Date of Acceptance: 31-Aug-2022
URI: http://hdl.handle.net/10044/1/99563
ISSN: 2398-6352
Publisher: Nature Research
Start Page: 1
End Page: 9
Journal / Book Title: npj Digital Medicine
Volume: 5
Issue: 143
Copyright Statement: © The Author(s) 2022. Open Access 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/.
Sponsor/Funder: Wellcome Trust
Funder's Grant Number: 203928/Z/16/Z
Keywords: Science & Technology
Life Sciences & Biomedicine
Health Care Sciences & Services
Medical Informatics
MACHINE
SYSTEM
Publication Status: Published
Online Publication Date: 2022-09-15
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine
Institute of Global Health Innovation



This item is licensed under a Creative Commons License Creative Commons