ReviewEuropean radiology2021
How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts.
Review in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Machine Learning-Based Prediction of Performance Gaps in Rowing and Identification of Key Training Monitoring Indicators.Sensors (Basel, Switzerland) · 2026Article
- Noninvasive prediction of lymph-vascular space invasion in cervical cancer based on ultrasomics nomogram.BMC cancer · 2026Article
- A quantitative study of pathologists' perceptions towards artificial intelligence-assisted diagnostic system.PLOS digital health · 2025Article
- Machine learning in dentistry and oral surgery: charting the course with bibliometric insights.Head & face medicine · 2025Article
- Current Technological Advances in Dysphagia Screening: Systematic Scoping Review.Journal of medical Internet research · 2025Article
- Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes.European radiology · 2025Article
- Predicting immunotherapy response in stage III-IV non-small cell lung cancer using integrated radiomics and clinical features.Frontiers in oncology · 2025Article
- Using apparent diffusion coefficient maps and radiomics to predict pathological grade in upper urinary tract urothelial carcinoma.BMC medical imaging · 2024Article
- Validation and adaptation of the moral outrage scale in the Peruvian adult population.BMC psychology · 2024Article
- Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.BMJ open · 2024Article
- An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer.Frontiers in oncology · 2024Article
- Construction and validation of infection risk model for patients with external ventricular drainage: a multicenter retrospective study.Acta neurochirurgica · 2023Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- The Quality and Utility of Artificial Intelligence in Patient Care.Deutsches Arzteblatt international · 2023Review
- Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography.European radiology · 2023Article
- CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII.Insights into imaging · 2023Article
- Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease.NPJ Parkinson's disease · 2022Article
- Review
- Radiomics Features Based on MRI-ADC Maps of Patients with Breast Cancer: Relationship with Lesion Size, Features Stability, and Model Accuracy.Medeniyet medical journal · 2022Article
- Examination of the diaphragm in obstructive sleep apnea using ultrasound imaging.Sleep & breathing = Schlaf & Atmung · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting.Key Points• Machine learning is new and rather complex for the radiology community.• Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.• Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.
Indexed as
Identifiers
33006018What 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.