ReviewDiagnostic and interventional radiology (Ankara, Turkey)2022
Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics.
Review in Diagnostic and interventional radiology (Ankara, Turkey), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Magnetic Resonance Imaging in Cerebral Small Vessel Disease-Related Depression: From Visual Scoring to Artificial Intelligence.CNS neuroscience & therapeutics · 2026Review
- Measuring the optimistic bias of cross-validation in radiomics.Scientific reports · 2026Article
- Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Personalised Dosimetry in Nuclear Medicine: Bridging Physics, Biology and AI for Next Generation Radiopharmaceutical Therapy.Nuclear medicine and molecular imaging · 2026Review
- From Flow to Feature Using a Proof-of-Concept Spectral-Driven Machine Learning Approach Using Smart Urinary and Drainage Catheter Systems: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Emerging Artificial Intelligence Models for Estimating Breslow Thickness from Dermoscopic Images.Biomedicines · 2026Review
- AI-driven healthcare: a trend toward better healthcare or the emergence of public health burden.Frontiers in digital health · 2026Article
- Cervical lymph node response to 131I therapy in differentiated thyroid cancer using radiomics and clinical features.Frontiers in oncology · 2026Article
- Artificial Intelligence-Based Hospital Malnutrition Screening: Validation of a Novel Machine Learning Model.Applied clinical informatics · 2025Article
- Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.Journal of neurology · 2025Review
- The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Radiomics analysis for the early diagnosis of common sexually transmitted infections and skin lesions.PLOS digital health · 2025Article
- Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation.Findings of ACL. NAACL · 2025Article
- Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study.Intensive care medicine experimental · 2025Article
- Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes.European radiology · 2025Article
- From advanced imaging to molecular insights: the state of the art in omics for axial spondyloarthritis.Therapeutic advances in musculoskeletal disease · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research to deal with large and complex imaging data sets. Nowadays, ML tools have become easily accessible to anyone. Such a low threshold to accessibility might lead to inappropriate usage and misinterpretation, without a clear intention. Therefore, ensuring methodological rigor is of paramount importance. Getting closer to the real-world clinical implementation of AI, a basic understanding of the main concepts should be a must for every radiology professional. In this respect, simplified explanations of the key concepts along with pitfalls and recommendations would be helpful for general radiology community to develop and improve their AI mindset. In this work, twenty-two key issues are reviewed within three categories: pre-modeling, modeling, and post-modeling. Firstly, the concept is shortly defined for each issue. Then, related common pitfalls and best practices are provided. Specifically, the issues included in this paper were validity of scientific question, unrepresentative samples, sample size, missing data, quality of reference standard, batch effect, reliability of features, feature scaling, multi-collinearity, class imbalance, data and target leakage, high-dimensional data, optimization, overfitting, generalization, performance metrics, clinical utility, comparison with conventional statistical and clinical methods, interpretability and explainability, randomness, transparent reporting, and sharing data.
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.