The Integration of Radiomics and Artificial Intelligence in Modern Medicine
Antonino Maniaci,
Salvatore Lavalle,
Caterina Gagliano,
Mario Lentini,
Edoardo Masiello,
Federica Parisi,
Giannicola Iannella,
Nicole Dalia Cilia,
Valerio Salerno,
Giacomo Cusumano,
Luigi La Via
Affiliations
Antonino Maniaci
Faculty of Medicine and Surgery, University of Enna “Kore”, 94100 Enna, Italy
Salvatore Lavalle
Faculty of Medicine and Surgery, University of Enna “Kore”, 94100 Enna, Italy
Caterina Gagliano
Faculty of Medicine and Surgery, University of Enna “Kore”, 94100 Enna, Italy
Mario Lentini
ASP Ragusa, Hospital Giovanni Paolo II, 97100 Ragusa, Italy
Edoardo Masiello
Radiology Unit, Department Clinical and Experimental, Experimental Imaging Center, Vita-Salute San Raffaele University, 20132 Milan, Italy
Federica Parisi
Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia”, ENT Section, University of Catania, Via S. Sofia, 78, 95125 Catania, Italy
Giannicola Iannella
Department of ‘Organi di Senso’, University “Sapienza”, Viale dell’Università, 33, 00185 Rome, Italy
Nicole Dalia Cilia
Department of Computer Engineering, University of Enna “Kore”, 94100 Enna, Italy
Valerio Salerno
Department of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy
Giacomo Cusumano
University Hospital Policlinico “G. Rodolico—San Marco”, 95123 Catania, Italy
Luigi La Via
University Hospital Policlinico “G. Rodolico—San Marco”, 95123 Catania, Italy
With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the nature of diseases, how well patients respond to treatment and patient outcomes. The use of AI techniques in radiomics, such as machine learning and deep learning, has made it possible to create sophisticated computer-aided diagnostic systems, predictive models, and decision support tools. The many uses of AI in radiomics are examined in this review, encompassing its involvement of quantitative feature extraction from medical images, the machine learning, deep learning and computer-aided diagnostic (CAD) systems approaches in radiomics, and the effect of radiomics and AI on improving workflow automation and efficiency, optimize clinical trials and patient stratification. This review also covers the predictive modeling improvement by machine learning in radiomics, the multimodal integration and enhanced deep learning architectures, and the regulatory and clinical adoption considerations for radiomics-based CAD. Particular emphasis is given to the enormous potential for enhancing diagnosis precision, treatment personalization, and overall patient outcomes.
