Interventional Radiology ex-machina: impact of Artificial Intelligence on practice.
Interventional Radiology ex-machina: impact of Artificial Intelligence on practice.
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DOI：
10.1007/s11547-021-01351-x
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发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Carrafiello G
中科院分区：
文献类型：
--
作者：
Gurgitano M;Angileri SA;Rodà GM;Liguori A;Pandolfi M;Ierardi AM;Wood BJ;Carrafiello G
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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DOI：
10.1148/rg.2017160130
发表时间：
2017-03
期刊：
Radiographics : a review publication of the Radiological Society of North America, Inc
影响因子：
--
作者：
Erickson BJ;Korfiatis P;Akkus Z;Kline TL
通讯作者：
Kline TL
影响因子：
2.9
作者：
Daye, Dania;Staziaki, Pedro, V;Uppot, Raul Nirmal
通讯作者：
Uppot, Raul Nirmal
影响因子：
5.4
作者：
Cho, Hyungjoo;Lee, June-Goo;Park, Seung-Jung
通讯作者：
Park, Seung-Jung
影响因子：
4.4
作者：
Erdal BS;Prevedello LM;Qian S;Demirer M;Little K;Ryu J;O'Donnell T;White RD
通讯作者：
White RD
影响因子：
3.7
作者：
Forkert ND;Verleger T;Cheng B;Thomalla G;Hilgetag CC;Fiehler J
通讯作者：
Fiehler J