Artificial Intelligence: A New Frontier in Radiological Imaging

¹ Student of Master in Hospital Administration, Datta Meghe Medical Sciences, Wanadongri, Nagpur, Maharashtra, India
² Professor Dept. of Biochemistry Dr. Rajendra Gode Medical College, Amravati
³ Professor Dept. of Anesthesiology Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences Sawangi Meghe, Wardha
⁴ Computer Technology Assistant Professor, Yeshwantrao Chavan College of Engineering, Nagpur

^* Corresponding author: ajayrenald@outlook.com

Abstract

Artificial intelligence (AI) is the development of computer systems that perform tasks that traditionally require human intelligence. One of the applications of AI is to help technologists and radiologists select appropriate patient protocols. Using AI methods, the accuracy of radiologists' diagnosis improved significantly by 37%. Currently, research is underway on the use of artificial intelligence in diagnostic medical imaging, which has demonstrated high sensitivity and accuracy in the identification of imaging abnormalities. In addition, artificial intelligence has the potential to improve tissue detection and characterization. Although the terms “artificial intelligence” and “machine learning” are often used interchangeably, it is important to note that machine learning is a specific subset of AI focusing on the use of algorithms to learn from the acquired data, enabling prediction, classification and understanding generation. With machine learning, a formal set of methodologies is based on solid mathematical foundations. The study of inventing and implementing algorithms that can learn from prior experiences is known as machine learning (ML). If you've observed a pattern of behaviour before, you can predict whether or not it'll happen again. That is, no prognosis can be made if no past examples exist. The major benefits of using machine learning in radiology will be the reduction of professional time and the accuracy of diagnostic outcomes. When compared to well-trained and experienced radiologists and technicians, several Al-based image segmentation methods in radiology systems have exhibited equivalent, if not better, performance.