Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future.
B M Zeeshan HameedGayathri PrerepaVathsala PatilPranav ShekharSyed Zahid RazaHadis KarimiRahul PaulNithesh NaikSachin ModiGanesh VigneswaranBhavan Prasad RaiPiotr ChłostaBhaskar Kumar SomaniPublished in: Therapeutic advances in urology (2021)
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.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- big data
- computed tomography
- convolutional neural network
- dual energy
- high resolution
- public health
- current status
- electronic health record
- endothelial cells
- positron emission tomography
- image quality
- climate change
- contrast enhanced
- photodynamic therapy
- data analysis
- risk assessment
- magnetic resonance
- mass spectrometry
- optical coherence tomography
- pluripotent stem cells