AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.

Journal: Journal of medical imaging and radiation oncology
Published Date: Jul 9, 2025

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.

Authors

  • S Carriero
    Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • R Canella
    Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
  • F Cicchetti
    Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy.
  • A Angileri
    Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • A Bruno
    Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Ancona, Italy.
  • P Biondetti
    Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • R R Colciago
    School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy.
  • A D'Antonio
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli, Italy.
  • G Della Pepa
    Breast Radiology Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milano, Italy.
  • F Grassi
  • V Granata
  • C Lanza
    Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • S Santicchia
    Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • A Miceli
    15Internal Medicine Department, Papardo Hospital, Messina, Italy.
  • A Piras
    UO Radioterapia Oncologica, Palermo, Italy.
  • V Salvestrini
    Radiation Oncology Unit, Oncology Department, Azienda Ospedaliero Universitaria Careggi, Florence, Italy.
  • G Santo
    Nuclear Medicine Unit, Department of Experimental and Clinical Medicine, "Magna Graecia" University of Catanzaro, Catanzaro, Italy.
  • F Pesapane
    Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
  • A Barile
  • G Carrafiello
    Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • A Giovagnoni

Keywords

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