ReviewMedical principles and practice : international journal of the Kuwait University, Health Science Centre2026
Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists.
Review in Medical principles and practice : international journal of the Kuwait University, Health Science Centre, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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0 citing papers in PubMed.
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Authors and funding
5 authors.
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Abstract
Artificial intelligence is increasingly embedded within radiology workflows. In radiology, large language models may support reporting and communication tasks, while machine learning and deep learning models are increasingly used for image classification, segmentation, and quantitative analysis. This article is an educational review and practical guide rather than an experimental study. It summarizes how convolutional neural networks work, outlines the key stages of model development and validation, and discusses how modern language models may assist with code prototyping and troubleshooting. Although these tools may reduce technical barriers, clinically useful models still require appropriate datasets, external validation, transparent reporting, governance, and human oversight. This review aimed to help radiologists understand the principles, potential, and limitations of convolutional neural networks in contemporary practice.
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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.