AI-Driven Radiomics: Revolutionizing Early Detection of Subclinical Pathologies in Multi-Modality Imaging
DOI:
https://doi.org/10.52783/jns.v14.2812Keywords:
AI-driven radiomics, subclinical pathologies, multi-modality imaging, radiological sciences, ultrasound technology, ultrasound training, machine learning, deep learning, precision medicine, medical imaging, feature extraction, predictive analyticsAbstract
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.
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Najjar, R. (2023). Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics, 13(17), 2760.
McCague, C., Ramlee, S., Reinius, M., Selby, I., Hulse, D., Piyatissa, P., ... & Woitek, R. (2023). Introduction to radiomics for a clinical audience. Clinical Radiology, 78(2), 83-98.
Huang, Y., Liu, Z., He, L., Chen, X., Pan, D., Ma, Z., ... & Liang, C. (2016). Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non—small cell lung cancer. Radiology, 281(3), 947-957.
Waite, S., Grigorian, A., Alexander, R. G., Macknik, S. L., Carrasco, M., Heeger, D. J., & Martinez-Conde, S. (2019). Analysis of perceptual expertise in radiology–current knowledge and a new perspective. Frontiers in human neuroscience, 13, 213.
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., ... & Pei, Y. (2024). A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis. Bioengineering, 11(3), 219.
Bhandari, A. (2024). Revolutionizing Radiology With Artificial Intelligence. Cureus, 16(10), e72646.
Mansouri, N. (2023). Simultaneous multimodal imaging of metabolic and functional parameters of tumors; tumor heterogeneity, prediction of precision medicine effectiveness by radiomic analysis and machine learning algorithms (Doctoral dissertation, Université Paris Cité).
Tran, W. T., Jerzak, K., Lu, F. I., Klein, J., Tabbarah, S., Lagree, A., ... & Sadeghi-Naini, A. (2019). Personalized breast cancer treatments using artificial intelligence in radiomics and pathomics. Journal of medical imaging and radiation sciences, 50(4), S32-S41.
Jain, P., Mohanty, S. K., & Saxena, S. (2025). AI in radiomics and radiogenomics for neuro-oncology: Achievements and challenges. Radiomics and Radiogenomics in Neuro-Oncology, 301-324.
Scapicchio, C., Gabelloni, M., Barucci, A., Cioni, D., Saba, L., & Neri, E. (2021). A deep look into radiomics. La radiologia medica, 126(10), 1296-1311.
Bhandari, A. (2024). Revolutionizing Radiology With Artificial Intelligence. Cureus, 16(10), e72646.
Alanazi, M. M. F., Almutairi, S. F. M., Alarjani, N. O., Alghaylan, M. Y. A., Aljawhari, M. S. M., & Alkhulaifi, A. A. S. (2024). Advancements in AI-driven diagnostic radiology: Enhancing accuracy and efficiency. International journal of health sciences, 5(S2), 1402-1414.
Zhang, Y. P., Zhang, X. Y., Cheng, Y. T., Li, B., Teng, X. Z., Zhang, J., ... & Cai, J. (2023). Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Military Medical Research, 10(1), 22.
Rasool, S., Husnain, A., Saeed, A., Gill, A. Y., & Hussain, H. K. (2023). Harnessing predictive power: exploring the crucial role of machine learning in early disease detection. JURIHUM: Jurnal Inovasi dan Humaniora, 1(2), 302-315.
Riquelme, D., & Akhloufi, M. A. (2020). Deep learning for lung cancer nodules detection and classification in CT scans. Ai, 1(1), 28-67.
Wang, X., & Zhu, H. (2024). Artificial Intelligence in Image-based Cardiovascular Disease Analysis: A Comprehensive Survey and Future Outlook. arXiv preprint arXiv:2402.03394.
D'Alessandro, T. (2024). Handwriting Analysis for the Development of a system to support the Diagnosis of Neurodegenerative Diseases.
Prabha, C. (2025). Early detection approach for analysis of osteoarthritis using artificial intelligence and machine learning. In Diagnosing Musculoskeletal Conditions using Artifical Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging (pp. 147-165). Academic Press.
Bhandari, A. (2024). Revolutionizing Radiology With Artificial Intelligence. Cureus, 16(10), e72646.
Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022). Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nature reviews Clinical oncology, 19(2), 132-146.
Seeram, E., & Kanade, V. (2024). Artificial Intelligence in Medical Imaging Technology: An Introduction. Springer Nature.
Wittek, A., Strizek, B., & Recker, F. (2024). Innovations in ultrasound training in obstetrics. Archives of Gynecology and Obstetrics, 1-10.
Jain, P., Mohanty, S. K., & Saxena, S. (2025). AI in radiomics and radiogenomics for neuro-oncology: Achievements and challenges. Radiomics and Radiogenomics in Neuro-Oncology, 301-324.
Stefano, A. (2024). Challenges and limitations in applying radiomics to PET imaging: possible opportunities and avenues for research. Computers in Biology and Medicine, 179, 108827.
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