ReviewFrontiers in digital health2022
Clinical deployment environments: Five pillars of translational machine learning for health.
Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Digital integration of research conduct into clinical care: results of the PROSPECTOR randomised feasibility study.BMJ evidence-based medicine · 2025Trial
- Optimising antibiotic switching via forecasting of patient physiology.Nature communications · 2026Article
- Use of Electronic Patient Record Systems for Rapid Response to an MHRA Public Assessment Report: Retrospective Observational Study.JMIR formative research · 2026Observational
- From scanner to scalpel: adapting IDEAL for accountable AI in radiology and image-guided neurosurgery.Frontiers in radiology · 2026Article
- From plug-and-play to institution-calibrated radiology AI: a practical framework for operationalizing local validation, monitoring and governance.Frontiers in radiology · 2026Article
- Expected Values of Artificial Intelligence in Colon Capsule Endoscopy: Artificial Intelligence in Capsule Endoscopy Consortium Position Statement.Endoscopy international open · 2026Article
- Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology.European radiology · 2025Review
- Design and implementation of a natural language processing system at the point of care: MiADE (medical information AI data extractor).BMC medical informatics and decision making · 2025Article
- Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review.Journal of medical Internet research · 2025Article
- Development and prospective evaluation of a machine learning model to predict serious cardiac outcomes among paediatric cardiac inpatients.BMJ digital health & AI · 2025Article
- Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions.Frontiers in digital health · 2025Article
- A roadmap to implementing machine learning in healthcare: from concept to practice.Frontiers in digital health · 2025Article
- Leveraging machine learning in nursing: innovations, challenges, and ethical insights.Frontiers in digital health · 2025Review
- An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation.Proceedings of machine learning research · 2024Article
- An electronic health record (EHR)-based risk calculator can predict fractures comparably to FRAX: a proof-of-concept study.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Article
- Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction.Frontiers in digital health · 2024Article
- Human Factors and Organizational Issues: Contributions from 2022.Yearbook of medical informatics · 2023Article
- Artificial Intelligence in Public Health: Current Trends and Future Possibilities.International journal of environmental research and public health · 2022Article
- Integrating Artificial Intelligence into Psychological Counseling: A Narrative Review and Governance Framework.Inquiry : a journal of medical care organization, provision and financingReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from "real-world" data to "real-world" development.
Indexed as
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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.