Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance
Open accessAbstract
Background: As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time. Objective: Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360° continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles. Discussion: We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.
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These frameworks primarily focus on evidence generation at the point of evaluation and provide limited guidance on how performance degradation should be detected and managed during routine clinical operation, particularly when outcome labels are delayed, incomplete, or influenced by the model's own use.
“However, these frameworks primarily focus on evidence generation at the point of evaluation and provide limited guidance on how performance degradation should be detected and managed during routine clinical operation, particularly when outcome labels are delayed, incomplete, or influenced by the model’s own use [25–27].”
From Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems (2026) · cites this paper as [26]
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- Authors
- Davis, S. E., Embí, P. J., & Matheny, M. E.
- Affiliations
- Vanderbilt University Medical Center (US)
- Veterans Health Administration (US)
- Corresponding author
- Davis, Sharon E
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-02-29
- Volume
- 31
- Issue
- 5
- Pages
- 1195-1198
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC11031208
- OpenAlex
- W4392282341
- Cited by
- 46
- References
- 14
- Citations by year
- 2026: 25 · 2025: 17 · 2024: 4
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD103537)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- clinical prediction tool, model maintenance, algorithmic monitoring, distribution shift, model drift, algorithmic oversight, algorithmic accountability, model life cycle, clinical implementation of AI
- MeSH terms
- Artificial Intelligence, Emotions
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