AI-Driven Radiomics: Revolutionizing Early Detection of Subclinical Pathologies in Multi-Modality Imaging
Year: 2025
Type: article
Abstract: Artificial Intelligence (AI)-driven radiomics is transforming the landscape of medical imaging by providing advanced tools for early detection of subclinical pathologies. By extracting vast amounts of imaging data and analyzing high-dimensional radiomic features, AI enhances diagnostic precision across multiple imaging modalities, including X-ray, CT, MRI, and ultrasound. The application of AI-driven radiomics has significant implications in radiological sciences, radiological technology—especially ultrasound—and ultrasound training programs. This review explores the role of AI-powered radiomics in multi-modality imaging, focusing on its ability to detect diseases at subclinical stages, optimize diagnostic workflows, and enhance training methodologies. The integration of AI with radiomics offers unprecedented opportunities for improving healthcare outcomes, yet it also presents challenges such as data standardization, regulatory hurdles, and model interpretability. This article provides an in-depth examination of AI-driven radiomics, its applications, benefits, and future directions in precision medicine and medical imaging.
Source: Journal of Neonatal Surgery
Authors: Manar Abdulrahman Alhusaini, Anwar Ali Alahmari, Asma Sulaiman Aldubayyan, Alaa Fouad Ahmed Alghamdi, Wafa Bohi Alabdely, Kholoud Saeed Aljarrah, Manar Ali Faqihi
Institution: Riyadh Armed Forces Hospital
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409115021