Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists
Year: 2026
Type: article
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.
Source: Medical Principles and Practice
DOI: 10.1159/000552704
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7162111453