Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure. (November 2021)

• Record Type: Journal Article
• Authors: Retico, Alessandra; Avanzo, Michele; Boccali, Tommaso; Bonacorsi, Daniele; Botta, Francesca; Cuttone, Giacomo; Martelli, Barbara; Salomoni, Davide; Spiga, Daniele; Trianni, Annalisa; Stasi, Michele; Iori, Mauro; Talamonti, Cinzia
• Is Part Of: Physica medica. Volume 91(2021)
• Page Start: 140
• Page End: 150
• Publication Date: 2021-11
• Subjects: ACC Alleanza Contro il Cancro (Alliance Against Cancer) -- AI Artificial Intelligence -- AIFM Associazione Italiana di Fisica Medica (Italian Association of Medical Physicists) -- ANN Artificial Neural Networks -- ARPA Agenzia Regionale per la Protezione dell'Ambiente (Regional Agency for Environment Protection) -- ASL Azienda Sanitaria Locale (Local Sanitary Agency) -- CAE Convolutional Auto Encoders -- CAMS Copernicus Atmosphere Monitoring Service -- CNN Convolutional Neural Networks -- DL Deep Learning -- DSS Decision Support Systems -- EDG European Data Grid -- EHR Electronic Health Records -- EPIC Enhanced PrIvacy and Compliance -- FAIR Findable, Accessible, Interoperable and Reusable -- GDPR General Data Protection Regulation -- GPU Graphical Processing Units -- GPGPU general-purpose GPU -- HEP High-Energy Physics -- INFN Istituto Nazionale di Fisica Nucleare (Italian National Institute for Nuclear Physics) -- ISS Istituto Superiore di Sanità (Italian National Institute of Health) -- ISTAT Istituto nazionale di Statistica (Italian National Institute of Statistics) -- ML Machine Learning -- PLANET Pollution Lake ANalysis for Effective Therapy -- QA Quality Assurance -- RT Radiation Therapy -- SVM Support Vector Machines -- XAI explainable AI; Artificial intelligence -- Decision support systems -- Computing infrastructure -- Distributed learning; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.10.005
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Radiomics and Artificial Intelligence are powerful tools for medical data analysis. Training predictive models needs access to data and suitable computing resources. Data should be FAIR (Findable, Accessible, Interoperable and Reusable). An easy-to-use cloud-based IT infrastructure can help train and validate AI models. INFN and AIFM jointly support the development of a dedicated infrastructure for AI. 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.