Implementation framework for AI deployment at scale in healthcare systems
Summary: Artificial intelligence (AI) and digital health technologies are increasingly used in the medical field. Despite promises of leading the future of personalized medicine and better clinical outcomes, implementation of AI faces barriers for deployment at scale. We introduce a novel implementa...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225006674 |
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| author | Hassan Sami Adnan Amitis Shidani Lei Clifton Clare R. Bankhead Rafael Perera-Salazar |
| author_facet | Hassan Sami Adnan Amitis Shidani Lei Clifton Clare R. Bankhead Rafael Perera-Salazar |
| author_sort | Hassan Sami Adnan |
| collection | DOAJ |
| description | Summary: Artificial intelligence (AI) and digital health technologies are increasingly used in the medical field. Despite promises of leading the future of personalized medicine and better clinical outcomes, implementation of AI faces barriers for deployment at scale. We introduce a novel implementation framework that can facilitate digital health designers, developers, patient groups, policymakers, and other stakeholders, to co-create and solve issues throughout the life cycle of designing, developing, deploying, monitoring, and maintaining algorithmic models. This framework targets health systems that integrate multiple machine learning (ML) models with various modalities. This design thinking approach promotes clinical utility beyond model prediction, combining privacy preservation with clinical parameters to establish a reward function for reinforcement learning, ranking competing models. This allows leveraging explainable AI (xAI) methods for clinical interpretability. Governance mechanisms and orchestration platforms can be integrated to monitor and manage models. The proposed framework guides users toward human-centered AI design and developing AI-enhanced health system solutions. |
| format | Article |
| id | doaj-art-d4e41f24178b40b5914fb43c78cc83b1 |
| institution | Kabale University |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-d4e41f24178b40b5914fb43c78cc83b12025-08-20T03:48:32ZengElsevieriScience2589-00422025-05-0128511240610.1016/j.isci.2025.112406Implementation framework for AI deployment at scale in healthcare systemsHassan Sami Adnan0Amitis Shidani1Lei Clifton2Clare R. Bankhead3Rafael Perera-Salazar4Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; Corresponding authorDepartment of Statistics, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKSummary: Artificial intelligence (AI) and digital health technologies are increasingly used in the medical field. Despite promises of leading the future of personalized medicine and better clinical outcomes, implementation of AI faces barriers for deployment at scale. We introduce a novel implementation framework that can facilitate digital health designers, developers, patient groups, policymakers, and other stakeholders, to co-create and solve issues throughout the life cycle of designing, developing, deploying, monitoring, and maintaining algorithmic models. This framework targets health systems that integrate multiple machine learning (ML) models with various modalities. This design thinking approach promotes clinical utility beyond model prediction, combining privacy preservation with clinical parameters to establish a reward function for reinforcement learning, ranking competing models. This allows leveraging explainable AI (xAI) methods for clinical interpretability. Governance mechanisms and orchestration platforms can be integrated to monitor and manage models. The proposed framework guides users toward human-centered AI design and developing AI-enhanced health system solutions.http://www.sciencedirect.com/science/article/pii/S2589004225006674Public healthArtificial intelligenceMachine learning |
| spellingShingle | Hassan Sami Adnan Amitis Shidani Lei Clifton Clare R. Bankhead Rafael Perera-Salazar Implementation framework for AI deployment at scale in healthcare systems iScience Public health Artificial intelligence Machine learning |
| title | Implementation framework for AI deployment at scale in healthcare systems |
| title_full | Implementation framework for AI deployment at scale in healthcare systems |
| title_fullStr | Implementation framework for AI deployment at scale in healthcare systems |
| title_full_unstemmed | Implementation framework for AI deployment at scale in healthcare systems |
| title_short | Implementation framework for AI deployment at scale in healthcare systems |
| title_sort | implementation framework for ai deployment at scale in healthcare systems |
| topic | Public health Artificial intelligence Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225006674 |
| work_keys_str_mv | AT hassansamiadnan implementationframeworkforaideploymentatscaleinhealthcaresystems AT amitisshidani implementationframeworkforaideploymentatscaleinhealthcaresystems AT leiclifton implementationframeworkforaideploymentatscaleinhealthcaresystems AT clarerbankhead implementationframeworkforaideploymentatscaleinhealthcaresystems AT rafaelpererasalazar implementationframeworkforaideploymentatscaleinhealthcaresystems |