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

Artificial intelligence Computing infrastructure Decision support systems Distributed learning

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)
ISSN: 1724-191X
Titre abrégé: Phys Med
Pays: Italy
ID NLM: 9302888

Informations de publication

Date de publication:
Nov 2021
Historique:
received: 26 05 2021
revised: 04 10 2021
accepted: 05 10 2021
pubmed: 22 11 2021
medline: 1 12 2021
entrez: 21 11 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 34801873
pii: S1120-1797(21)00320-3
doi: 10.1016/j.ejmp.2021.10.005
pii:
doi:

Types de publication

Letter

Langues

eng

Sous-ensembles de citation

IM

Pagination

140-150

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auteurs

Alessandra Retico (A)

National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy.

Michele Avanzo (M)

Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy.

Tommaso Boccali (T)

National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy.

Daniele Bonacorsi (D)

University of Bologna, 40126 Bologna, Italy; INFN, Bologna Division, 40126 Bologna, Italy.

Francesca Botta (F)

Medical Physics Unit, Istituto Europeo di oncologia IRCCS, 20141 Milan, Italy.

Giacomo Cuttone (G)

INFN, Southern National Laboratory (LNS), 95123 Catania, Italy.

Barbara Martelli (B)

INFN, CNAF Division, 40126 Bologna, Italy.

Davide Salomoni (D)

INFN, CNAF Division, 40126 Bologna, Italy.

Daniele Spiga (D)

INFN, Perugia Division, 06123 Perugia, Italy.

Annalisa Trianni (A)

Medical Physics Unit, Ospedale Santa Chiara APSS, 38122 Trento, Italy.

Michele Stasi (M)

Medical Physics Unit, A.O. Ordine Mauriziano di Torino, 10128 Torino, Italy.

Mauro Iori (M)

Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy. Electronic address: mauro.iori@ausl.re.it.

Cinzia Talamonti (C)

Department Biomedical Experimental and Clinical Science "Mario Serio", University of Florence, 50134 Florence, Italy; INFN, Florence Division, 50134 Florence, Italy.

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Classifications MeSH