Interventional Radiology ex-machina: impact of Artificial Intelligence on practice.

Artificial intelligence (AI) Augmented reality (AR) Deep learning (DL) Interventional radiology (IR) Machine learning (ML) Virtual reality (VR)

Journal

La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625

Informations de publication

Date de publication:
Jul 2021
Historique:
received: 20 10 2020
accepted: 24 03 2021
pubmed: 17 4 2021
medline: 23 6 2021
entrez: 16 4 2021
Statut: ppublish

Résumé

Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in larger data after exposure to examples; Deep Learning uses neural networks in order to extract meaningful patterns from imaging data, even deciphering that which would otherwise be beyond human perception. Thus, AI has the potential to revolutionize the healthcare systems and clinical practice of doctors all over the world. This is especially true for radiologists, who are integral to diagnostic medicine, helping to customize treatments and triage resources with maximum effectiveness. Related in spirit to Artificial intelligence are Augmented Reality, mixed reality, or Virtual Reality, which are able to enhance accuracy of minimally invasive treatments in image guided therapies by Interventional Radiologists. The potential applications of AI in IR go beyond computer vision and diagnosis, to include screening and modeling of patient selection, predictive tools for treatment planning and navigation, and training tools. Although no new technology is widely embraced, AI may provide opportunities to enhance radiology service and improve patient care, if studied, validated, and applied appropriately.

Identifiants

pubmed: 33861421
doi: 10.1007/s11547-021-01351-x
pii: 10.1007/s11547-021-01351-x
pmc: PMC8050998
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

998-1006

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Auteurs

Martina Gurgitano (M)

Operative Unit of Radiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, via Francesco Sforza 35, 20122, Milan, Italia. martina.gurgitano@gmail.com.

Salvatore Alessio Angileri (SA)

Operative Unit of Radiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, via Francesco Sforza 35, 20122, Milan, Italia.

Giovanni Maria Rodà (GM)

Postgraduation School in Radiodiagnostics, Università Degli Studi di Milano, via Festa del Perdono, 20122, Milan, Italy.

Alessandro Liguori (A)

Operative Unit of Radiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, via Francesco Sforza 35, 20122, Milan, Italia.

Marco Pandolfi (M)

Operative Unit of Radiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, via Francesco Sforza 35, 20122, Milan, Italia.

Anna Maria Ierardi (AM)

Operative Unit of Radiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, via Francesco Sforza 35, 20122, Milan, Italia.

Bradford J Wood (BJ)

Center for Interventional Oncology, National Institutes of Health Clinical Center and National Cancer Institute, National Institutes of Health, 10 Center Dr., Room 1C-341, MSC 1182, Bethesda, MD, 20892, USA.

Gianpaolo Carrafiello (G)

Operative Unit of Radiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, via Francesco Sforza 35, 20122, Milan, Italia.
Department of Health Sciences, Università Degli Studi di Milano, Milan, Italy.

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