Role of artificial intelligence in medical radiology and imaging

• Published in: MGM Journal of Medical Sciences Vol. 11; no. 3; pp. 558 - 564
• Main Authors: Arfat, Mohd, Nisha, T. K., Sahu, Sapna, Rashid, Mohd
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer - Medknow 01.07.2024; Medknow Publications and Media Pvt. Ltd; Wolters Kluwer Medknow Publications
• Edition: 3
• Subjects: Algorithms; Artificial intelligence; artificial intelligence; artificial neural networks; deep learning; radiology and imaging; Computational linguistics; Coronaviruses; Data mining; Epidemics; Epidemiology; Forecasts and trends; Health aspects; India; Language processing; Machine learning; Medical care; Medical imaging equipment; Medical research; Medicine, Experimental; Natural language interfaces; Quality management; Radiology; Radiology, Medical; Review Article; India; artificial neural networks; deep learning; radiology and imaging; Artificial intelligence
• ISSN: 2347-7946; 2347-7962; 2347-7962
• DOI: 10.4103/mgmj.mgmj_187_24

Abstract Artificial intelligence (AI) is revolutionizing radiology, oncology, and other medicine and veterinary care areas. Adopting deep learning algorithms has significantly advanced image analysis and disease detection. This study explores how AI is reshaping the roles of radiologists and radiographers. It highlights its vital function in infection detection and control, as evidenced by its impact during the coronavirus disease 2019 (COVID-19) pandemic. In veterinary radiation oncology, AI supports complex contouring and treatment planning. However, while AI offers numerous advantages, its implementation must be cautiously approached. Radiologists face challenges, particularly the overwhelming volume of imaging data, which AI helps manage through artificial neural networks and machine learning (ML) algorithms-two significant innovations in this field. In veterinary radiation oncology, AI facilitates collaboration, standardization of data, and the creation of standard operating procedures. Early disease detection, enabled by AI, is essential for initiating treatments that can improve patient outcomes and prognosis. AI is crucial in analyzing large medical datasets, including imaging and clinical data, through advanced algorithms and ML techniques. In veterinary medicine, AI is key to addressing complex challenges in host-pathogen interactions, precision medicine, and predictive epidemiology. AI-powered solutions for continuous monitoring ensure that at-risk patients receive ongoing observation, enabling the rapid detection of changes in health markers. This approach is especially advantageous in managing chronic conditions, enabling proactive healthcare, and facilitating early intervention.