ReviewDiagnostics (Basel, Switzerland)2025
Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.
Review in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.BMC medical imaging · 2026Pooled it
- Volumetric reference data of the orbit: a deep learning MRI analysis in the German national cohort.Scientific reports · 2026Article
- Deep Learning-Based Panoramic Radiograph Retrieval from Antemortem Images for Forensic Identification.International journal of legal medicine · 2026Article
- An uncertainty-aware vision transformer-BiLSTM Bayesian framework for reliable clinical decision support using chest X-rays.Scientific reports · 2026Article
- A Real-Time Digital Twin Synchronization Framework for Multi-Sensor Cardiopulmonary Resuscitation Measurement.Sensors (Basel, Switzerland) · 2026Article
- Review
- Review
- Artificial intelligence in neurology practice: promise, perils, and a roadmap for responsible integration.Journal of neurology · 2026Review
- Efficacy and comparative performance of machine learning models for stroke risk prediction in hypertensive patients: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- MRI segmentation of head and neck tumors using hybrid attention mechanism and dense dilated spatial pyramid pooling.Journal of applied clinical medical physics · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness.Bioengineering (Basel, Switzerland) · 2025Article
- AI-Driven Clinical Decision Support System for Automated Ventriculomegaly Classification from Fetal Brain MRI.Journal of imaging · 2025Article
- Broken Rotor Bar Fault Detection for Inverter-Fed Induction Motor with Negative-Sequence Current Analysis.Sensors (Basel, Switzerland) · 2025Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Utilization of BiLSTM- and GAN-Based Deep Neural Networks for Automated Power Amplifier Optimization over X-Parameters.Sensors (Basel, Switzerland) · 2025Article
- An Innovative Medical Image Analyzer Incorporating Fuzzy Approaches to Support Medical Decision-Making.Medical sciences (Basel, Switzerland) · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- Beyond visual inspection: the deep learning revolution in quantitative cerebrovascular imaging.Frontiers in neuroscience · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has revolutionised medical image analysis, offering the possibility of automated, efficient, and highly accurate diagnostic solutions. This article explores recent developments in deep learning techniques applied to medical imaging, including convolutional neural networks (CNNs) for classification and segmentation, recurrent neural networks (RNNs) for temporal analysis, autoencoders for feature extraction, and generative adversarial networks (GANs) for image synthesis and augmentation. Additionally, U-Net models for segmentation, vision transformers (ViTs) for global feature extraction, and hybrid models integrating multiple architectures are explored. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process were used, and searches on PubMed, Google Scholar, and Scopus databases were conducted. The findings highlight key challenges such as data availability, interpretability, overfitting, and computational requirements. While deep learning has demonstrated significant potential in enhancing diagnostic accuracy across multiple medical imaging modalities-including MRI, CT, US, and X-ray-factors such as model trust, data privacy, and ethical considerations remain ongoing concerns. The study underscores the importance of integrating multimodal data, improving computational efficiency, and advancing explainability to facilitate broader clinical adoption. Future research directions emphasize optimising deep learning models for real-time applications, enhancing interpretability, and integrating deep learning with existing healthcare frameworks for improved patient outcomes.
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.