Interventional Radiology Ex-Machina: Impact of Artificial Intelligence on Practice
Open accessAbstract
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.
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Many other domains of medicine beyond transplantation could benefit from AI, including precision medicine, AI-assisted computer vision, augmented and virtual reality, and AI-assisted integration and collection of patients' records.
“Moreover, while this manuscript focuses on the use of AI in transplantation, many other domains could benefit from it.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [62]
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References 58 · Cited by 100
Research works printed but not linked: 1
- [5]Kumar V, Gu Y, Basu S et al (2013) NIH Public Access 30:1234–1248. 10.1016/j.mri.2012.06.010.QIN · DOI
- Authors
- Gurgitano, M., Angileri, S. A., Rodà, G. M., Liguori, A., Pandolfi, M., Ierardi, A. M., Wood, B. J., & Carrafiello, G.
- Affiliations
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT)
- University of Milan (IT)
- National Institutes of Health (US)
- Corresponding author
- Gurgitano, Martina
- ORCID
- Venue
- La radiologia medica
- Published
- 2021-04-16
- Volume
- 126
- Issue
- 7
- Pages
- 998-1006
- ISSN
- 0033-8362 (ISSN-L)
- 1826-6983
- PMC
- PMC8050998
- OpenAlex
- W3156038334
- Cited by
- 100
- References
- 58
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 20 · 2023: 36 · 2022: 19 · 2021: 6
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- La radiologia medica (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Nvidia
- National Institutes of Health (COVID-19)
- National Institute of Allergy and Infectious Diseases (COVID-19)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, interventional radiology, deep learning, machine learning, augmented reality, mixed reality, virtual reality, image-guided therapy, computer vision, patient selection, radiology education, minimally invasive treatment
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Humans, Radiology, Interventional
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