From code to continuous care: Key considerations for developing and implementing dependable AI prediction models in healthcare delivery

Qiaochu Wei; Miao Cui; Yujuan Qi; Qiang Zhang; Zhonghua Liu; Canqing Yu; F.D. Richard Hobbs; David C. Christiani; Yi Li; Ruyang Zhang; Feng Chen; Guoshuang Feng; Yuantao Hao; Yongyue Wei

doi:10.59717/j.xinn-med.2026.100229

The Innovation Medicine > 2026 Vol. 4 > No. 3 > 100229

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From code to continuous care: Key considerations for developing and implementing dependable AI prediction models in healthcare delivery

1.
Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing 100191, China
2.
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
3.
Qinghai Provincial people's Hospital, Qinghai Province Research Center for High Altitude Medicine, Xining, Qinghai 810001, China
4.
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York NY 10032, USA
5.
Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
6.
Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford OX2 6GG, UK
7.
Oxford Institute of Digital Health, University of Oxford, Oxford OX2 6GG, UK
8.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston MA 02115, USA
9.
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
10.
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
11.
Big Data Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health,Beijing 100045, China

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Corresponding authors: haoyt@bjmu.edu.cn (Y.H.); fengguoshuang@mail.ccmu.edu.cn (G.F.)
Received Date: February 07, 2026

Accepted Date: June 10, 2026

Published Online: June 11, 2026

The Innovation Medicine 4(3), https://doi.org/10.59717/j.xinn-med.2026.100229 | Cite this article

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PUBLIC SUMMARY
1. Care delivery needs the Development, Implementation, And MONitoring for Dependable AI model framework.
  
  Development requires clear use cases, reliable data, standardized predictors, and rigorous validation.
  
  Implementation requires workflow integration, transparent governance, fairness, and accountability.
  
  Monitoring requires ongoing evaluation, model updates, and cost-effectiveness assessment.
  
  The proposed DIAMOND checklist offers a practical path from promising algorithms to dependable care.

ABSTRACT

ABSTRACT

Predictive models in healthcare are widely published, yet few achieve routine clinical use due to gaps in methodological rigor, workflow integration, and governance. Existing guidelines primarily focus on clinical settings, with few addressing broader healthcare delivery contexts. We propose a practical framework for translating code to continuous care: Development, Implementation, And MONitoring for Dependable AI prediction model (DIAMOND). Models must be built for explicit clinical use cases, supported by interoperable data, standardized predictors, and rigorous validation with prospective designs. Translation into practice requires workflow integration, proportionate regulatory oversight of intended use, transparency, uncertainty, bias, and accountability, and continued post-deployment evaluation—testing transportability across settings, monitoring and updating for data shift and performance degradation, and assessing health-economic impact to inform iterative refinement. By systematically linking these stages, the DIAMOND framework provides a structured pathway for advancing AI predictive models from promising algorithms to dependable clinical tools.

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Wei Q., Cui M., Qi Y., et al. (2026). From code to continuous care: Key considerations for developing and implementing dependable AI prediction models in healthcare delivery. The Innovation Medicine 4:100229. https://doi.org/10.59717/j.xinn-med.2026.100229

Wei Q., Cui M., Qi Y., et al. (2026). From code to continuous care: Key considerations for developing and implementing dependable AI prediction models in healthcare delivery. The Innovation Medicine 4:100229. https://doi.org/10.59717/j.xinn-med.2026.100229

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