Redefining Radiology - Artificial Intelligence Integration in Medical Imaging

Abstract

Undoubtedly, medical imaging research is currently dominated by discussions on Artificial Intelligence (AI), encompassing both diagnostic and therapeutic aspects. In the realm of diagnostic imaging alone, the quantity of AI-related publications has surged from approximately 100 - 150 annually during the period of 2007 - 2008 to 1000 - 1100 annually in the years 2017 - 2018. Researchers have successfully utilized AI to automatically identify intricate patterns in imaging data and provide quantitative evaluations of radiographic attributes. Within the field of radiation oncology, AI has found application across various image modalities employed at distinct stages of treatment, such as tumor delineation and treatment assessment. Radiomics, a technique involving the extraction of a vast array of image features from radiation images using a high-throughput approach, currently stands as one of the most prominent research areas within medical imaging. AI serves as an indispensable catalyst in processing immense quantities of medical images, thereby unveiling disease characteristics that may elude human perception. This paper aims to provide a comprehensive overview of the historical progression of AI in medical imaging research, its current role, the challenges that must be addressed before widespread adoption in clinical settings, and its potential future. The paper also advocates the ongoing research, the embrace of cutting-edge imaging technologies, and the cultivation of strong collaborations between radiologists and AI developers.