Radiology Assisted by Artificial Intelligence: Current Condition and Analysis

Abstract

Artificial intelligence (AI) is transforming industries that have historically relied on human cognitive talents, and humanity is reaching a turning point in its technological revolution. Advances in artificial neural networks have fueled this shift, which has changed data processing and evaluation, opening possibilities for using AI solutions to handle challenging and time-consuming activities. Convolutional networks (CNNs) and GPU technology adoption. By improving accuracy and computing efficiency, has already completely transformed image recognition. AI techniques are especially useful in radiology for jobs involving pattern. Improved detection and categorization; for instance, automated feature extraction using AI technologies has improved diagnostic efficiency and accuracy in identifying anomalies across imaging modalities. According to our data, the main priority areas for AI solutions are CT and MRI modalities, neuroimaging, and chest imaging, which reflects their high clinical demand and complexity. Additionally, high-prevalence diseases are targeted with AI technologies, illnesses, including breast cancer, stroke, and lung cancer, highlighting AI's compatibility with significant diagnostic requirements. With most products authorized under the Medical Device Directive (MDD) and Medical Device Regulation (MDR) in Class IIa or Class I categories, indicating compliance with moderate-risk requirements, the regulatory environment plays a crucial role in the development of AI technologies. From 2017 to 2020, there was a sharp rise in the creation of AI products, which peaked in 2020 and then recently stabilized and saturated. The authors of this paper examine the developments in AI based imaging applications, highlighting AI's revolutionary potential for improved diagnostic support and concentrating on CNNs' crucial function, legal issues, and possible risks to human labor in the diagnostic imaging industry

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