Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications
Open accessAbstract
With rapidly evolving artificial intelligence solutions, healthcare organizations need an implementation roadmap. A "clinical trials" informed approach can promote safe and impactful implementation of artificial intelligence. This framework includes four phases: (1) Safety; (2) Efficacy; (3) Effectiveness and comparison to an existing standard; and (4) Monitoring. Combined with inter-institutional collaboration and national funding support, this approach will advance safe, usable, effective, and equitable deployments of artificial intelligence in healthcare.
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Evidence from activated systems undergoing longitudinal monitoring provides the strongest basis for judging whether a system remains safe, equitable, clinically useful, and governable once embedded in everyday care.
“Guidance documents can define what should matter; deployment-proximal evaluations can show whether activation appears plausible in a target setting; however, evidence from activated systems undergoing longitudinal monitoring provides the strongest basis for judging whether a system remains safe, equitable, clinically useful, and governable once embedded in everyday care.”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [15]
The existence of such frameworks does not ensure robust post-deployment oversight in routine care.
“However, the existence of such frameworks does not ensure robust post-deployment oversight in routine care”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [15]
Recent implementation-oriented perspectives emphasise that safe deployment depends on structured governance, staged review, and durable post-deployment accountability rather than one-time technical approval alone.
“Recent implementation-oriented perspectives make a similar point by emphasising that safe deployment depends on structured governance, staged review, and durable post-deployment accountability rather than one-time technical approval alone [15,50,51].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [15]
Uptake of formal guidance in clinical AI trials remains incomplete.
“and uptake of formal guidance in clinical AI trials remains incomplete [15,16].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [15]
The Introduction identifies SPIRIT-AI and CONSORT-AI as reporting guidelines for clinical AI trials, but does not say how often trials follow them. The Discussion contrasts those guidelines with the paper’s implementation framework; neither section addresses uptake. Searches for adoption, adherence, and compliance found no passage establishing that uptake remains incomplete. (LLM, conf=0.73)
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Lee, J.-Y., In, Y.-H., Kim, J. H., Hwang, S. M., Lee, J.-J., & Kwon, Y. S. (2026) · Diagnostics · doi:10.3390/diagnostics16193082
Fotouhi, A., Shah-Mohammadi-Loshani, M., Khosravi, A., Ranjbar-Niavol, F., Hassan, M., Zarrabi, A., & Vosough, M. (2026) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2026.109030
Basanta, H., Singh, T. H., Singh, P. T., Wu, W.-C., Kang, E. Y., Singh, S. A., Laikhuram, A., Huang, C., Huang, W.-C., Chun, P.-P., et al. (2026) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2026.10.008
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- Authors
- You, J. G., Hernandez-Boussard, T., Pfeffer, M. A., Landman, A., & Mishuris, R. G.
- Affiliations
- Harvard University (US)
- Stanford Medicine (US)
- Corresponding author
- You, Jacqueline G.
- Venue
- npj Digital Medicine
- Published
- 2025-02-17
- Volume
- 8
- Issue
- 1
- Pages
- 107-107
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC11832725
- OpenAlex
- W4407633448
- Cited by
- 59
- References
- 33
- Citations by year
- 2026: 46 · 2025: 13
- FWCI
- 3.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- npj Digital Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- U.S. National Library of Medicine (T15LM007092)
- National Institutes of Health (UL1TR003142)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in healthcare, clinical implementation, AI safety, effectiveness, monitoring, implementation roadmap, inter-institutional collaboration, national funding
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