AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.
Authors
Affiliations (19)
Affiliations (19)
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy.
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Ancona, Italy.
- School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy.
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli, Italy.
- Breast Radiology Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milano, Italy.
- Division of Radiology, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy.
- Istituto Nazionale Tumori, IRCCS, Fondazione Pascale, Napoli, Italy.
- Nuclear Medicine Unit, Azienda Ospedaliero-Universitaria SS. Antonio e Biagio e Cesare Arrigo, Alessandria, Italy.
- UO Radioterapia Oncologica, Palermo, Italy.
- RI.MED Foundation, Palermo, Italy.
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, Palermo, Italy.
- Radiation Oncology, Mater Olbia Hospital, Olbia, Italy.
- Radiation Oncology Unit, Oncology Department, Azienda Ospedaliero Universitaria Careggi, Florence, Italy.
- Nuclear Medicine Unit, Department of Experimental and Clinical Medicine, "Magna Graecia" University of Catanzaro, Catanzaro, Italy.
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
- Department of Radiology, Ospedali Riuniti, Università Politecnica Delle Marche, Ancona, Italy.
Abstract
The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is transforming diagnostic and therapeutic processes. AI-driven tools, especially in deep learning and machine learning, have shown remarkable potential in enhancing image recognition, analysis and decision-making. This technological advancement allows for the automation of routine tasks, improved diagnostic accuracy, and the reduction of human error, leading to more efficient workflows. Moreover, the successful implementation of AI in healthcare requires comprehensive education and training for young clinicians, with a pressing need to incorporate AI into residency programmes, ensuring that future specialists are equipped with traditional skills and a deep understanding of AI technologies and their clinical applications. This includes knowledge of software, data analysis, imaging informatics and ethical considerations surrounding AI use in medicine. By fostering interdisciplinary integration and emphasising AI education, healthcare professionals can fully harness AI's potential to improve patient outcomes and advance the field of medical imaging and therapy. This review aims to evaluate how AI influences radiology, nuclear medicine and radiation oncology, while highlighting the necessity for specialised AI training in medical education to ensure its successful clinical integration.