Can AI-powered imaging be a replacement for radiologists?

Abstract

Artificial Intelligence (AI) has a wide range of potential uses in medical imaging, despite many clinical implementation challenges. AI can enhance a radiologist's productivity by prioritizing work lists, for example, AI can automatically examine chest X-rays for pneumothorax and evidence of intracranial hemorrhage, Alzheimer's disease, and urinary stones. AI may be used to automatically quantify skeletal maturity on pediatric hand radiographs, coronary calcium scoring, prostate categorization through MRI, breast density via mammography, and ventricle segmentation via cardiac MRI. The usage of AI covers almost the full spectrum of medical imaging. AI is gaining traction not as a replacement for a radiologist but as an essential companion or tool. The possible applications of AI in medical imaging are numerous and include the full medical imaging life cycle, from picture production to diagnosis to prediction of outcome. The availability of sufficiently vast, curated, and representative training data to train, evaluate, and test algorithms optimally are some of the most significant barriers to AI algorithm development and clinical adoption, but they can be resolved in upcoming years through the creation of data libraries. Therefore, AI is not a competitor, but a friend in need of radiologists who can use it to deal with day-to-day jobs and concentrate on more challenging cases. All these aspects of interactions between AI and human resources in the field of medical imaging are discussed in this chapter.

Ähnliche Arbeiten