- Autorzy:
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Prasad Rai, Bhavan
Zahid Raza, Syed
Patil, Vathsala
Hameed, B.M. Zeeshan
Karimi, Hadis
Vigneswaran, Ganesh
Somani, Bhaskar K.
Chłosta, Piotr
Modi, Sachin
Prerepa, Gayathri
Naik, Nithesh
Shekhar, Pranav
Paul, Rahul - Opis:
- 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.
- Dostawca treści:
- Repozytorium Uniwersytetu Jagiellońskiego
Artykuł