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

Year: 2026
Type: article
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.
DOI: 10.59717/j.xinn-med.2026.100229
Authors: 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
Institutions: Peking University, Qinghai Provincial Peoples Hospital, Columbia University, University of Oxford, Harvard University, University of Michigan, Nanjing Medical University, Capital Medical University, Beijing Children’s Hospital
Field: Medicine
Open Access status: diamond
OpenAlex ID: W7167367975
FWCI:
Cites: 71
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