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dc.contributor.authorKoch, Miriam
dc.contributor.authorArlandini, Claudio
dc.contributor.authorAntonopoulos, Gregory
dc.contributor.authorBaretta, Alessia
dc.contributor.authorBeaujean, Pierre
dc.contributor.authorBex, Geert Jan
dc.contributor.authorBiancolini, Marco Evangelos
dc.contributor.authorCeli, Simona
dc.contributor.authorCosta, Emiliano
dc.contributor.authorDrescher, Lukas
dc.contributor.authorEleftheriadis, Vasileios
dc.contributor.authorFadel, Nur A.
dc.contributor.authorFink, Andreas
dc.contributor.authorGalbiati, Federica
dc.contributor.authorHatzakis, Ilias
dc.contributor.authorHompis, Georgios
dc.contributor.authorLewandowski, Natalie
dc.contributor.authorMemmolo, Antonio
dc.contributor.authorMensch, Carl
dc.contributor.authorObrist, Dominik
dc.contributor.authorPaneta, Valentina
dc.contributor.authorPapadimitroulas, Panagiotis
dc.contributor.authorPetropoulos, Konstantinos
dc.contributor.authorPorziani, Stefano
dc.contributor.authorSavvidis, Georgios
dc.contributor.authorSethia, Khyati
dc.contributor.authorStrakoš, Petr
dc.contributor.authorSvobodová, Petra
dc.contributor.authorVignali, Emanuele
dc.date.accessioned2024-03-01T07:32:05Z
dc.date.available2024-03-01T07:32:05Z
dc.date.issued2023
dc.identifier.citationTechnology and Health Care. 2023, vol. 31, issue 4, p. 1509-1523.cs
dc.identifier.issn0928-7329
dc.identifier.issn1878-7401
dc.identifier.urihttp://hdl.handle.net/10084/152270
dc.description.abstractBACKGROUND: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers. These areas can be (and already are) supported by High-Performance-Computing (HPC), High-Performance Data Analytics (HPDA), and AI (together “HPC+”). OBJECTIVE: This overview article aims to show state-of-the-art examples of studies supported by the National Competence Centres (NCCs) in HPC+ within the EuroCC project, employing HPC, HPDA and AI for medical applications. METHOD: The included studies on different applications of HPC in the medical sector were sourced from the National Competence Centres in HPC and compiled into an overview article. Methods include the application of HPC+ for medical image processing, high-performance medical and pharmaceutical data analytics, an application for pediatric dosimetry, and a cloud-based HPC platform to support systemic pulmonary shunting procedures. RESULTS: This article showcases state-of-the-art applications and large-scale data analytics in the medical sector employing HPC+ within surgery, medical image processing in diagnostics, nutritional support of patients in hospitals, treating congenital heart diseases in children, and within basic research. CONCLUSION: HPC+ support scientific fields from research to industrial applications in the medical area, enabling researchers to run faster and more complex calculations, simulations and data analyses for the direct benefit of patients, doctors, clinicians and as an accelerator for medical research.cs
dc.language.isoencs
dc.publisherIOS Presscs
dc.relation.ispartofseriesTechnology and Health Carecs
dc.relation.urihttps://doi.org/10.3233/THC-229015cs
dc.rights© 2023 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4.0).cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/cs
dc.subjectcomputer simulationcs
dc.subjectcomputational modelingcs
dc.subjectdata analysiscs
dc.subjectAI (artificial intelligence)cs
dc.subjectmedicinecs
dc.subjecttherapeuticscs
dc.subjectdiagnosiscs
dc.titleHPC+ in the medical field: Overview and current examplescs
dc.typearticlecs
dc.identifier.doi10.3233/THC-229015
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume31cs
dc.description.issue4cs
dc.description.lastpage1523cs
dc.description.firstpage1509cs
dc.identifier.wos001029075200032


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© 2023 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4.0).
Except where otherwise noted, this item's license is described as © 2023 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4.0).