ReviewRadiology. Artificial intelligence2023
A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging.
Review in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 4 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
122 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Rapid evaporative ionization mass spectrometry in surgery: a systematic review.The British journal of surgery · 2025Pooled it
- Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data.American journal of rhinology & allergy · 2025Pooled it
- Stage-stratified benefits of AI-radiomics PET in early Alzheimer's disease: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- Evaluating machine learning algorithms based on thermal imaging and milk-based parameters to identify subclinical mastitis in dairy cows.The veterinary quarterly · 2026Article
- Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications.The veterinary quarterly · 2026Review
- Multicohort Evaluation of Acquisition Confounding in Single-Site Lung-CT Malignancy Classification with Frozen Foundation-Model Features.Journal of imaging informatics in medicine · 2026Article
- Radiomics and machine learning for differentiating pediatric intramedullary spinal tumors: a pilot study.European radiology experimental · 2026Article
- Predicting antibacterial activity of silver nanoparticles using physicochemical descriptors.iScience · 2026Article
- Article
- Evaluation-design effects and reference-CT-defined HU-class errors in brain MRI-to-CT synthesis on SynthRAD2023: a technical note.Radiological physics and technology · 2026Article
- TSNet: a multi-modal deep learning framework for subtyping appendicitis: integrating ultrasound images, handcrafted features, and clinical data.Abdominal radiology (New York) · 2026Article
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- A study on eye movement trajectory classification for strabismus screening via integration of global dependencies and temporal dynamics.Medical & biological engineering & computing · 2026Article
- Comparison of radiomics-based models for detection of Modic type 1 changes in photon-counting detector CT images of the lumbar spine.Skeletal radiology · 2026Article
- Article
- Markerless Video-Based Gait Analysis for Motor Phenotyping in Individuals with Schizophrenia Using Interpretable Machine Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Domain Generalization of Histopathology Foundation Models in Multicenter, Multi-Scanner Cohorts: A Comparative Benchmark.Journal of imaging · 2026Article
- Three-class radiomic differentiation of hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma on multiphasic contrast-enhanced CT.Abdominal radiology (New York) · 2026Article
- Accuracy of Distal Internal Carotid Artery Contrast Ratio to Infer Proximal Carotid Disease in the Mobile Stroke Unit.Stroke (Hoboken, N.J.) · 2026Article
62 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is being increasingly used to automate and improve technologies within the field of medical imaging. A critical step in the development of an AI algorithm is estimating its prediction error through cross-validation (CV). The use of CV can help prevent overoptimism in AI algorithms and can mitigate certain biases associated with hyperparameter tuning and algorithm selection. This article introduces the principles of CV and provides a practical guide on the use of CV for AI algorithm development in medical imaging. Different CV techniques are described, as well as their advantages and disadvantages under different scenarios. Common pitfalls in prediction error estimation and guidance on how to avoid them are also discussed.
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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.