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Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

npj Digital Medicine, vol. 5, pp. 2

Abstract

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy. PubMed, Web of Science, and the ACM Digital Library were searched, and AI experts were consulted. Topics were extracted from the identified literature and summarized across the six phases at the core of this review: (1) data preparation, (2) AIPM development, (3) AIPM validation, (4) software development, (5) AIPM impact assessment, and (6) AIPM implementation into daily healthcare practice. From 2683 unique hits, 72 relevant guidance documents were identified. Substantial guidance was found for data preparation, AIPM development and AIPM validation (phases 1-3), while later phases clearly have received less attention (software development, impact assessment and implementation) in the scientific literature. The six phases of the AIPM development, evaluation and implementation cycle provide a framework for responsible introduction of AI-based prediction models in healthcare. Additional domain and technology specific research may be necessary and more practical experience with implementing AIPMs is needed to support further guidance.

Authors 14

  1. Leiden University Medical Center

    Affiliation as printed

    Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, The Netherlands. a.a.h.de_hond@lumc.nl

    Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. a.a.h.de_hond@lumc.nl

    Department of Information Technology and Digital Innovation, Leiden University Medical Center, Leiden, The Netherlands. a.a.h.de_hond@lumc.nl

    Department of Information Technology and Digital Innovation, Leiden University Medical Center, Leiden, The Netherlands

  2. Artuur M. Leeuwenberg corresponding

    Utrecht University · University Medical Center Utrecht

    Affiliation as printed

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. a.m.leeuwenberg-15@umcutrecht.nl

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

  3. Utrecht University · University Medical Center Utrecht

    Affiliation as printed

    Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

  4. Leiden University Medical Center

    Affiliation as printed

    Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, The Netherlands

    Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

    Department of Information Technology and Digital Innovation, Leiden University Medical Center, Leiden, The Netherlands

  5. Utrecht University · University Medical Center Utrecht

    Affiliation as printed

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

  6. Leiden University Medical Center

    Affiliation as printed

    Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, The Netherlands

    National eHealth Living Lab, Leiden, The Netherlands

  7. Leiden University Medical Center

    Affiliation as printed

    Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands

    National eHealth Living Lab, Leiden, The Netherlands

  8. Utrecht University · University Medical Center Utrecht

    Affiliation as printed

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

  9. Utrecht University · University Medical Center Utrecht

    Affiliation as printed

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

  10. Utrecht University · University Medical Center Utrecht

    Affiliation as printed

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

  11. Utrecht University · University Medical Center Utrecht

    Affiliation as printed

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

  12. Leiden University Medical Center

    Affiliation as printed

    Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, The Netherlands

    Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

  13. Leiden University Medical Center

    Affiliation as printed

    Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands

    National eHealth Living Lab, Leiden, The Netherlands

  14. Utrecht University · University Medical Center Utrecht

    Affiliation as printed

    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

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References 75