Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

B. M.Zeeshan Hameed, Gayathri Prerepa, Vathsala Patil, Pranav Shekhar, Syed Zahid Raza, Hadis Karimi, Rahul Paul, Nithesh Naik, Sachin Modi, Ganesh Vigneswaran, Bhavan Prasad Rai, Piotr Chłosta, Bhaskar K. Somani

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish
JournalTherapeutic Advances in Urology
Volume13
DOIs
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Urology

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