A framework for the oversight and local deployment of safe and high-quality prediction models
Open accessAbstract
Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can detect potential safety signals. There is a lack of consensus in establishing governance to deploy, pilot, and monitor algorithms within operational healthcare delivery workflows. Here, we describe a governance framework that combines current regulatory best practices and lifecycle management of predictive models being used for clinical care. Since January 2021, we have successfully added models to our governance portfolio and are currently managing 52 models.
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These assumptions may not hold equally across low- and middle-income countries, smaller hospitals, rural systems, or institutions with limited digital maturity, where the infrastructure required for continuous monitoring, subgroup surveillance, drift detection, formal change control, and incident response may be more difficult to sustain.
“These assumptions may not hold equally across low- and middle-income countries, smaller hospitals, rural systems, or institutions with limited digital maturity, where the infrastructure required for continuous monitoring, subgroup surveillance, drift detection, formal change control, and incident response may be more difficult to sustain [49,50,51,52].”
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 [51]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe feasibility of lifecycle governance is likely to vary substantially across health systems because many recommendations assume access to data infrastructure, informatics expertise, technical staffing, procurement capacity, legal support, regulatory maturity, and multidisciplinary oversight structures.
“The feasibility of lifecycle governance is likely to vary substantially across health systems because many recommendations assume access to data infrastructure, informatics expertise, technical staffing, procurement capacity, legal support, regulatory maturity, and multidisciplinary oversight structures [49,50,51,52].”
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 [51]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredChen et al. and Reddy et al. proposed a structure of subpanels, overseen by an executive-level overarching committee, each with specific foci to spearhead different aspects of evaluation throughout the AI lifecycle.
“Chen et al. and Reddy et al. proposed a structure of subpanels, overseen by an executive-level overarching committee, each with specific foci to spearhead different aspects of evaluation throughout the AI lifecycle8,17.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [8]
The ‘ABCDS oversight committee’ passage describes an executive-level committee overseeing three subcommittees with distinct responsibilities, and Table 1 specifies their evaluation, implementation and monitoring, and regulatory roles. The structure is supported, but this paper describes it as a framework established at Duke; it does not support attributing the proposal to Chen et al. and Reddy et al. (LLM, conf=0.84)
Experimental — accuracy not yet measuredEvidence 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 [51]
Comparable emphases on lifecycle management, local governance, and organisational readiness are visible in recent clinical AI implementation and oversight frameworks and in WHO guidance on AI for health.
“Comparable emphases on lifecycle management, local governance, and organisational readiness are visible in recent clinical AI implementation and oversight frameworks, as well as in WHO guidance on AI for health [50,51,52].”
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 [51]
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 [51]
The Bedoya et al. framework has been deployed in the Duke University Health System, and at the time of publication, 52 models were regulated via the framework.
“This framework has already been deployed in a healthcare system, the Duke University Health System, and at the time of publication, 52 models were regulated via the framework.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [8]
Bedoya et al. created a governance framework built around the AI/ML development and implementation process with several checkpoints, which act as software regulatory stop points.
“Bedoya et al.8, often referenced as ABCDS (Algorithm-Based CDS), created a governance framework built around the AI/ML development and implementation process with several checkpoints, which act as software regulatory stop points.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [8]
A wide variety of governance best practices in the form of frameworks, governance structures, checklists, and guidelines have been proposed.
“As AI solutions have emerged, a wide variety of governance best practices in the form of frameworks, governance structures, checklists, and guidelines have been proposed7–23.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [8]
A recurring theme across multiple articles was the necessity for an overarching governance body to assist in selecting AI tools and guide validation, implementation, and monitoring processes.
“A recurring theme across multiple articles was the necessity for an overarching governance body7–10,17. This entity would not only assist in selecting AI tools and software for implementation but also guide the validation, implementation, and monitoring processes.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [8]
Hond et al., Bedoya et al. (ABCDS framework), and Coombs et al. emphasized the importance of using either retrospective or prospective data for validation and generalizability assessment.
“Hond et al., Bedoya et al. (ABCDS framework), and Coombs et al. emphasized the importance of using either retrospective or prospective data for validation and generalizability assessment8,11,14.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [8]
Existing hospital governance models, model-documentation tools, patient-safety approaches, and reporting standards provide important components for institutional oversight.
“Existing hospital governance models, model-documentation tools, patient-safety approaches, and reporting standards also provide important components for institutional oversight [15][16][17][18][19][20][21][22][23][24].”
From The AI Hospital Formulary: A Practical Governance Framework for Prescribing, Monitoring, and Deprescribing Artificial Intelligence in Hospitals (2026) · cites this paper as [15]
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- Authors
- Bedoya, A. D., Economou-Zavlanos, N. J., Goldstein, B. A., Young, A., Jelovsek, J. E., O’Brien, C., Parrish, A. B., Elengold, S., Lytle, K., Balu, S., Huang, E., Poon, E. G., & Pencina, M. J.
- Affiliations
- Duke University (US)
- Duke University Health System (US)
- Corresponding author
- Bedoya, Armando D
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-05-31
- Volume
- 29
- Issue
- 9
- Pages
- 1631-1636
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC9382367
- OpenAlex
- W4282933928
- Cited by
- 119
- References
- 13
- Citations by year
- 2026: 38 · 2025: 34 · 2024: 25 · 2023: 20 · 2022: 1
- FWCI
- 6.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- AI governance, machine learning deployment, model monitoring, clinical workflow integration
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Delivery of Health Care
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