Artificial Intelligence in Radiology : What Is Its True Role at Present, and Where Is the Evidence? - 25/04/24

Doi : 10.1016/j.rcl.2024.03.008

Alireza Mohseni, MD ^a,^⁎ , Elena Ghotbi, MD ^a, Foad Kazemi, MD ^a, Amirali Shababi, BS ^b, Shayan Chashm Jahan, BS ^c, Anita Mohseni, BS ^d, Niloufar Shababi, MD ^a
^a Johns Hopkins University School of Medicine, 600 N. Wolfe Street / Phipps 446, Baltimore, MD 21287, USA
^b School of Medicine, Iran University of Medical Sciences, Hemat Highway next to Milad Tower 14535, Tehran, Iran
^c Department of Computer Science, University of Maryland, 8125 Paint Branch Drive College Park, MD 20742, USA
^d Azad University Tehran Medical Branch, Danesh, Shariati Street, Tehran, Iran 19395/1495

^∗Corresponding author. 600 N. Wolfe Street / Phipps 446, Baltimore, MD 21287.600 N. Wolfe Street / Phipps 446BaltimoreMD21287

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Thursday 25 April 2024
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Résumé

The integration of artificial intelligence (AI) in radiology has brought about substantial advancements and transformative potential in diagnostic imaging practices. This study presents an overview of the current research on the application of AI in radiology, highlighting key insights from recent studies and surveys. These recent studies have explored the expected impact of AI, encompassing machine learning and deep learning, on the work volume of diagnostic radiologists. The present and future role of AI in radiology holds great promise for enhancing diagnostic capabilities, improving workflow efficiency, and ultimately, advancing patient care.

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Keywords : Artificial intelligence, Radiology, Medical imaging, Machine learning, Deep learning, Radiomics