Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date:

Abstract

Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.

Authors

  • Alessandra Retico
    Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.
  • Michele Avanzo
    Division of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, PN, Italy.
  • Tommaso Boccali
    National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy.
  • Daniele Bonacorsi
    University of Bologna, 40126 Bologna, Italy; INFN, Bologna Division, 40126 Bologna, Italy.
  • Francesca Botta
    Medical Physics Unit, Istituto Europeo di oncologia IRCCS, 20141 Milan, Italy.
  • Giacomo Cuttone
    INFN, Southern National Laboratory (LNS), 95123 Catania, Italy.
  • Barbara Martelli
    INFN, CNAF Division, 40126 Bologna, Italy.
  • Davide Salomoni
    INFN, CNAF Division, 40126 Bologna, Italy.
  • Daniele Spiga
    INFN, Perugia Division, 06123 Perugia, Italy.
  • Annalisa Trianni
    Medical Physics Department of Provincial Agency for Health Services of the Autonomous Province of Trento, APSS, S. Chiara Hospital, Trento, 38121, Italy.
  • Michele Stasi
    Medical Physics Unit, A.O. Ordine Mauriziano di Torino, 10128 Torino, Italy.
  • Mauro Iori
    Medical Physics, Azienda USL-IRCCS di Reggio Emilia, Italy. Electronic address: mauro.iori@ausl.re.it.
  • Cinzia Talamonti
    Department of Biomedical Experimental Clinical Science "M. Serio", University of Florence, Florence, Italy.