ReviewNPJ digital medicine2023
Making machine learning matter to clinicians: model actionability in medical decision-making.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses 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
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies.Journal of medical Internet research · 2024Pooled it
- Unsupervised machine learning identifies distinct SLE patient endotypes with differential response to belimumab.Rheumatology (Oxford, England) · 2025Trial
- The Future of Cardiac Care: A Review on the Role for Non-Invasive, Home-Based Monitoring and Rehabilitation after Hospitalisation for Acute Decompensated Heart Failure.Current heart failure reports · 2026Review
- Stakeholder perspectives on implementing ED time series forecasting: a qualitative interview study.BMC emergency medicine · 2026Article
- Multi-source data integration through pooling and transfer learning improves generalizability and specialization of deep learning models for ICU mortality and length of stay prediction: a four-database external validation study.Critical care (London, England) · 2026Article
- Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.Intensive care medicine experimental · 2026Article
- Multicenter development and validation of machine-learning risk models to predict procedural complete revascularization and in-hospital heart failure in STEMI patients treated with primary PCI.Frontiers in cardiovascular medicine · 2026Article
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Clinical problem selection for machine learning-based clinical decision support in the intensive care unit: complexity, actionability, and the way forward.Frontiers in medicine · 2026Article
- Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study.Journal of medical Internet research · 2025Observational
- Entropy removal of clinical features.Scientific reports · 2025Article
- Nine quick tips for trustworthy machine learning in the biomedical sciences.PLoS computational biology · 2025Article
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
- Development and external validation of a predictive model for lung metastases in kidney cancer based on clinical and laboratory features.Clinical and experimental medicine · 2025Article
- Development of a risk prediction model for secondary infection in severe/critical COVID-19 patients.BMC infectious diseases · 2025Article
- Evaluating Machine Learning for Predicting Youth Suicidal Behavior Up to 1 Year After Contact With Mental-Health Specialty Care.Clinical psychological science : a journal of the Association for Psychological Science · 2025Article
- Evaluation of inflammatory-thrombosis panel as a diagnostic tool for vascular Behçet's disease.Clinical rheumatology · 2025Article
- AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Step-by-step causal analysis of EHRs to ground decision-making.PLOS digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has the potential to transform patient care and outcomes. However, there are important differences between measuring the performance of ML models in silico and usefulness at the point of care. One lens to use to evaluate models during early development is actionability, which is currently undervalued. We propose a metric for actionability intended to be used before the evaluation of calibration and ultimately decision curve analysis and calculation of net benefit. Our metric should be viewed as part of an overarching effort to increase the number of pragmatic tools that identify a model's possible clinical impacts.
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.