ArticleEuropean heart journal. Digital health2022
A clinician's guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML).
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Pooled it
- Self-Reported Post-COVID-19 Condition and Associated Factors Using Machine Learning Techniques: A Cross-Sectional Study.Medicina (Kaunas, Lithuania) · 2026Observational
- An explainable ensemble learning-based auxiliary diagnosis system for cerebral small vessel disease.Scientific reports · 2026Article
- Calibrated and Explainable CIN2+ Risk Stratification Using Routine Clinical Data: Development and External Validation.International journal of women's health · 2026Article
- Using Machine Learning to Predict Medication Therapy Problems among Patients with Chronic Kidney Disease.American journal of nephrology · 2026Article
- Data-driven classification of playing styles and match outcome prediction in UEFA Champions League teams.Biology of sport · 2026Article
- Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review.Journal of medical Internet research · 2025Article
- Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review.Journal of clinical medicine · 2025Review
- In defense of empathic informatics.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.Journal of endourology · 2025Article
- Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: A systematic review.PLOS digital health · 2025Article
- Raising awareness of potential biases in medical machine learning: Experience from a Datathon.PLOS digital health · 2025Article
- Explainable artificial intelligence for stroke risk stratification in atrial fibrillation.European heart journal. Digital health · 2025Review
- Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy.Scientific reports · 2025Article
- Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.Diabetologia · 2025Review
- Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review.European heart journal. Digital health · 2025Review
- Artificial intelligence in clinical medicine: a state-of-the-art overview of systematic reviews with methodological recommendations for improved reporting.Frontiers in digital health · 2025Review
- Practical Models for Predicting Vaginal Intraepithelial Neoplasia in High-Grade Squamous Intraepithelial Lesions Patients within Two years After Conization.International journal of women's health · 2025Article
- Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model.EClinicalMedicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside.
Indexed as
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.