ReviewPostgraduate medical journal2023
Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.
Review in Postgraduate medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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
27 citing papers in PubMed.
- Early retinal disease detection from fundus images using deep neural networks.Scientific reports · 2026Article
- Artificial intelligence in surgical care within low-income and middle-income countries: a scoping review of development, validation, and deployment.EClinicalMedicine · 2026Review
- When Lie Groups Meet Hyperspectral Images: Equivariant Manifold Network for Few-Shot HSI Classification.Sensors (Basel, Switzerland) · 2026Article
- Automated image-based classification of rat fecal consistency using an object-detection tool.BMC research notes · 2026Article
- Comparison of Deep Learning Architectures for Cardiac Contour Segmentation in Catheterization Radiographs.Cureus · 2026Article
- Agentic artificial intelligence in radiology workflow: from image interpretation to report quality control.Frontiers in medicine · 2026Review
- Advancing Medical Decision-Making with AI: A Comprehensive Exploration of the Evolution from Convolutional Neural Networks to Capsule Networks.Journal of imaging · 2025Review
- [Translational challenges and clinical potential of artificial intelligence in minimally invasive surgery].Chirurgie (Heidelberg, Germany) · 2025Article
- A Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-Attention Model Architecture for Precise Medical Image Analysis and Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives.Bioengineering (Basel, Switzerland) · 2025Review
- Convolutional neural network based system for fully automatic FLAIR MRI segmentation in multiple sclerosis diagnosis.Scientific reports · 2025Article
- Salivary and Microbiome Biomarkers in Periodontitis: Advances in Diagnosis and Therapy-A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Deep learning for automated boundary detection and segmentation in organ donation photography.Innovative surgical sciences · 2025Article
- Artificial Intelligence in Trauma and Orthopaedic Surgery: A Comprehensive Review From Diagnosis to Rehabilitation.Cureus · 2025Review
- Comparative evaluation of CAM methods for enhancing explainability in veterinary radiography.Scientific reports · 2025Article
- SmartGel OV: A NaturalMedicina (Kaunas, Lithuania) · 2025Observational
- A dual self-attentive transformer U-Net model for precise pancreatic segmentation and fat fraction estimation.BMC medical imaging · 2025Article
- Evaluating ChatGPT-4 Plus in Ophthalmology: Effect of Image Recognition and Domain-Specific Pretraining on Diagnostic Performance.Diagnostics (Basel, Switzerland) · 2025Article
- Burnout crisis in Chinese radiology: will artificial intelligence help?European radiology · 2025Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 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
6 authors.
Funding
Abstract
Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data. Understanding how these models work can help clinicians leverage their full potential, especially as artificial intelligence continues to evolve and integrate into healthcare. CNNs have already demonstrated their efficacy in diverse medical fields, including radiology, histopathology, and medical photography. In radiology, CNNs have been used to automate the assessment of conditions such as pneumonia, pulmonary embolism, and rectal cancer. In histopathology, CNNs have been used to assess and classify colorectal polyps, gastric epithelial tumours, as well as assist in the assessment of multiple malignancies. In medical photography, CNNs have been used to assess retinal diseases and skin conditions, and to detect gastric and colorectal polyps during endoscopic procedures. In surgical laparoscopy, they may provide intraoperative assistance to surgeons, helping interpret surgical anatomy and demonstrate safe dissection zones. The integration of CNNs into medical image analysis promises to enhance diagnostic accuracy, streamline workflow efficiency, and expand access to expert-level image analysis, contributing to the ultimate goal of delivering further improvements in patient and healthcare outcomes.
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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.