The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
To warrant clinical adoption AI models require a multi-faceted implementation evaluation
Abstract
Despite artificial intelligence (AI) technology progresses at unprecedented rate, our ability to translate these advancements into clinical value and adoption at the bedside remains comparatively limited. This paper reviews the current use of implementation outcomes in randomized controlled trials evaluating AI-based clinical decision support and found limited adoption. To advance trust and clinical adoption of AI, there is a need to bridge the gap between traditional quantitative metrics and implementation outcomes to better grasp the reasons behind the success or failure of AI systems and improve their translation into clinical value.
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“We were particularly interested in empirical studies with AI in the healthcare domain because of the need for human-subjects research in AI's development cycle and the importance of aligning algorithms with user needs and clinical workflows (Chen et al., 2022;van de Sande et al., 2024).”
From Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review (2025) · cites this paper as van de Sande et al., 2024
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Principles alone cannot guarantee ethical AI
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van de Sande, D., van Genderen, M. E., Huiskens, J., Gommers, D., & van Bommel, J. (2021) · Intensive Care Medicine · doi:10.1007/s00134-021-06446-7 · Cited by 279
Self-citationRandomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236
Zhou, Q., Chen, Z.-H., Cao, Y.-H., & Peng, S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00524-2 · Cited by 126
From Code to Bedside: Implementing Artificial Intelligence Using Quality Improvement Methods
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Marwaha, J. S., & Kvedar, J. C. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00572-2 · Cited by 46
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Cited by
Gomez, C., Cho, S. M., Ke, S., Huang, C.-M., & Unberath, M. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1521066 · Cited by 209
Berkhout, W. E. M., van Wijngaarden, J. J., Workum, J. D., van de Sande, D., Hilling, D. E., Jung, C., Meyfroidt, G., Gommers, D., Buijsman, S. N. R., & van Genderen, M. E. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.22866 · Cited by 75
Self-citationA Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools
Patel, H., Crusco, S., Hansen, D., Morales, J., Patale, T., Chiuzan, C., Sands, M., Naidich, J., Myers, K., Davidson, K. W., et al. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10381-y · Cited by 4
Pranata, Y., Mirwanti, R., Bahtiar, Y. A., & Purwanti, H. (2026) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s621469 · Cited by 1
Metadata
- Authors
- van de Sande, D., Chung, E. F. F., Oosterhoff, J., van Bommel, J., Gommers, D., & van Genderen, M. E.
- Venue
- npj Digital Medicine
- Published
- 2024-03-06
- Volume
- 7
- Issue
- 1
- ISSN
- 2398-6352
- Cited by
- 76
- References
- 13
- Language
- en
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
