ReviewMedical image analysis2022
Recent advances and clinical applications of deep learning in medical image analysis.
Review in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06081569 (Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease), which is not on this map. Cited by 343 papers, 11 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.
Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease
Who cites it
343 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Image-Based Deep Learning for Cataract Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- Research on machine learning-based clinical prediction models: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Artificial intelligence in diabetes management: transformative potential, challenges, and opportunities in healthcare.Hormones (Athens, Greece) · 2025Pooled it
- The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.European radiology · 2025Pooled it
- Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation-a systematic review, meta-analysis, and pilot evaluation of key results.Frontiers in medical technology · 2025Pooled it
- Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Accuracy of transfer learning in predicting peri-implantitis onset from intraoral radiograph: A multi-center longitudinal retrospective study.Clinical oral investigations · 2026Article
- Artificial Intelligence for Thyroid Nodule Diagnosis: From Multimodal Integration to Clinical Utility.Diagnostics (Basel, Switzerland) · 2026Review
- Deep learning-based automatic identification model of main pancreatic duct in intraoperative ultrasonography.Surgical endoscopy · 2026Article
- Deep Learning-Based Detection Model for Leukemia Cells in Peripheral Blood Smears Using YOLOv11-Large.Current oncology (Toronto, Ont.) · 2026Article
- Deep Learning-Assisted Accuracy Improvement in Bladder Cancer Staging of Spectrum-Aided Visual Enhanced Cystoscopy Images.Biosensors · 2026Article
- Predicting difficult laryngoscopy via three-dimensional graphical modeling and hybrid artificial intelligence using standard facial photographs in elective surgery patients.BMC anesthesiology · 2026Observational
- Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Application of transformer-enhanced convolutional neural network: multicenter MRI assessment of muscle invasion in bladder cancer.European radiology · 2026Article
- Generalist foundation models from a multimodal dataset for 3D computed tomography.Nature biomedical engineering · 2026Article
283 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
10 authors.
Funding
Abstract
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss major technical challenges and suggest possible solutions in the future research efforts.
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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.