TY - JOUR
T1 - Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements
T2 - radiology leading the way for future
AU - Hameed, B. M.Zeeshan
AU - Prerepa, Gayathri
AU - Patil, Vathsala
AU - Shekhar, Pranav
AU - Zahid Raza, Syed
AU - Karimi, Hadis
AU - Paul, Rahul
AU - Naik, Nithesh
AU - Modi, Sachin
AU - Vigneswaran, Ganesh
AU - Prasad Rai, Bhavan
AU - Chłosta, Piotr
AU - Somani, Bhaskar K.
N1 - Publisher Copyright:
© The Author(s), 2021.
PY - 2021
Y1 - 2021
N2 - Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.
AB - Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.
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U2 - 10.1177/17562872211044880
DO - 10.1177/17562872211044880
M3 - Review article
AN - SCOPUS:85115236699
SN - 1756-2872
VL - 13
JO - Therapeutic Advances in Urology
JF - Therapeutic Advances in Urology
ER -