ReviewMayo Clinic proceedings. Digital health2024
Machine Learning Operations in Health Care: A Scoping Review.
Review in Mayo Clinic proceedings. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Beyond Regulatory Approval: Lifecycle Assurance for Clinical Artificial Intelligence.Journal of medical systems · 2026Article
- Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation.IEEE transactions on bio-medical engineering · 2026Review
- Current status and future directions for the use of artificial intelligence in pediatric critical care nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Article
- Healthcare Professionals' Perceptions of AI-Assisted Clinical Decision-Making in Jordan: A Qualitative Study of Trust, Accountability, System Readiness, and Professional Practice.Healthcare (Basel, Switzerland) · 2026Article
- Prospective validation and real-time implementation of an automated machine learning postoperative mortality prediction model.British journal of anaesthesia · 2026Article
- Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance-A Scoping Review of Reviews.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence for predicting hospital admissions from the emergency department: a prospective, quasi-experimental study.Nature communications · 2026Article
- Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management.Annals of hematology · 2026Review
- Considering the missing science of retraining and maintenance in medical artificial intelligence, using ophthalmology as an exemplar.NPJ digital medicine · 2026Review
- Review
- Monitoring Deployed AI Systems in Health Care.ArXiv · 2026Article
- Analyzing and redesigning deployment pathways for internally developed artificial intelligence tools in clinical education and health research.Discover artificial intelligence · 2026Article
- Cost-Benefit Analysis of Preventing Acute Care Use in Oncology Patients Following Systemic Therapy Using Medicare Claims Data: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Kademlia hash snow ablation resource optimized stride scheduling for mobile computing services in healthcare sector.Scientific reports · 2025Article
- Equipping mathematical models for hospital dynamics using information theory.NPJ digital medicine · 2025Article
- Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts: Model Classification Study.JMIR bioinformatics and biotechnology · 2025Article
- Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill children.medRxiv : the preprint server for health sciences · 2025Article
- Artificial intelligence tool development: what clinicians need to know?BMC medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of machine learning tools in health care is rapidly expanding. However, the processes that support these tools in deployment, that is, machine learning operations, are still emerging. The purpose of this work was not only to provide a comprehensive synthesis of existing literature in the field but also to identify gaps and offer insights for adoption in clinical practice. A scoping review was conducted using the MEDLINE, PubMed, Google Scholar, Embase, and Scopus databases. We used MeSH and non-MeSH search terms to identify pertinent articles, with the authors performing 2 screening phases and assigning relevance scores: 148 English language articles most salient to the review were eligible for inclusion; 98 offered the most unique information and these were supplemented by 50 additional sources, yielding 148 references. From the 148 references, we distilled 7 key topic areas, based on a synthesis of the available literature and how that aligned with practitioner needs. The 7 topic areas were machine learning model monitoring; automated retraining systems; ethics, equity, and bias; clinical workflow integration; infrastructure, human resources, and technology stack; regulatory considerations; and financial considerations. This review provides an overview of best practices and knowledge gaps of this domain in health care and identifies the strengths and weaknesses of the literature, which may be useful to health care machine learning practitioners and consumers.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.