Clinical Artificial Intelligence Applications in Radiology : Neuro - 22/10/21

Doi : 10.1016/j.rcl.2021.07.002

Felipe Campos Kitamura, MD, MSc, PhD ^a,^b,^⁎ , Ian Pan, MD ^a,^c,^{^{1
Present address: 75 Francis St, Mellins Library, Boston, MA 02115, USA}}, Suely Fazio Ferraciolli, MD ^a,^{^{2
Present address: Rua Rua Gilberto Sabino, 215, 3rd floor, Pinheiros, São Paulo, São Paulo CEP: 05425-020, Brazil}}, Kristen W. Yeom, MD ^d,^{^{3
Present address: 725 Welch Road G516, Palo Alto, CA 94304, USA}}, Nitamar Abdala, MD, PhD ^b,^{^{4
Present address: Rua Napoleaão de Barros, 800, Vila Clementino, São Paulo, São Paulo CEP: 04024-002, Brazil}}
^a DasaInova, Diagnósticos da América SA (Dasa), São Paulo, São Paulo, Brazil
^b Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
^c Brigham and Woman's Hospital, Boston, MA, USA
^d Department of Radiology, Stanford University, CA, USA

^∗Corresponding author. Rua Rua Gilberto Sabino, 215, 3rd floor, Pinheiros, São Paulo, São Paulo CEP: 05425-020, Brazil.Rua Rua Gilberto Sabino, 215, 3rd floor, PinheirosSão PauloSão PauloCEP: 05425-020Brazil

Resumen

Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown. This article illustrates some of these applications. This article reviews machine learning challenges related to neuroradiology. The first approval of reimbursement for an AI algorithm by the Centers for Medicare and Medicaid Services, covering a stroke software for early detection of large vessel occlusion, is also discussed.

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Keywords : Neuroradiology, Machine learning, Deep learning, Artificial intelligence, Clinical applications