ReviewNPJ digital medicine2023
Self-supervised learning for medical image classification: a systematic review and implementation guidelines.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06018545 (AI Assisted Reader Evaluation in Acute CT Head Interpretation), which is not on this map. Cited by 150 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.
AI Assisted Reader Evaluation in Acute CT Head Interpretation
Who cites it
150 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Global warming amplifies atmospheric formaldehyde exposure and widens spatial inequality.Eco-Environment & Health · 2026Article
- A Facial Foundation Model for Clinical Biomarker Prediction and Real-World Mobile Deployment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ProtoSurv: prototype-guided adaptation of computed tomography foundation model for lung-cancer prognosis prediction.Visual computing for industry, biomedicine, and art · 2026Article
- MRI-based fetal gestational age estimation using a structure-aware self-supervised network.European radiology · 2026Article
- A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.Journal of healthcare informatics research · 2026Review
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Multimodal Nomogram for the Prenatal Risk Assessment of Hypoplastic Left Heart Syndrome Using Self-Supervised Learning.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- A multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma.Neuroradiology · 2026Article
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Leveraging Self-Supervised Learning for Non-Invasive Intra-Cardiac Magnetic Resonance Oximetry Assessment.medRxiv : the preprint server for health sciences · 2026Article
- Halo-free quantitative phase imaging via physics-constrained self-supervised network.Journal of biomedical optics · 2026Article
- Mitigating Spurious Invariance in Contrastive Learning: A Probabilistic Self-Supervised Learning Framework for Medical Image Analysis.IEEE transactions on medical imaging · 2026Article
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- BodyMAE: A Surface-Area Aware Masked Autoencoder for Body Composition Estimation from 3D Body Scans.medRxiv : the preprint server for health sciences · 2026Article
- Comparative Evaluation of Deep Learning and Foundation Model Embeddings for Osteoarthritis Feature Classification in Knee Radiographs.Journal of imaging informatics in medicine · 2026Article
- Proto-memformer: deformable memory transformer for Parkinson's MRI classification.Scientific reports · 2026Article
- Article
- Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
90 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
6 authors.
Funding
Abstract
Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires large quantities of labeled training data, which is both time-consuming and cost-prohibitive to curate for medical images. Self-supervised learning has the potential to make significant contributions to the development of robust medical imaging models through its ability to learn useful insights from copious medical datasets without labels. In this review, we provide consistent descriptions of different self-supervised learning strategies and compose a systematic review of papers published between 2012 and 2022 on PubMed, Scopus, and ArXiv that applied self-supervised learning to medical imaging classification. We screened a total of 412 relevant studies and included 79 papers for data extraction and analysis. With this comprehensive effort, we synthesize the collective knowledge of prior work and provide implementation guidelines for future researchers interested in applying self-supervised learning to their development of medical imaging classification models.
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.