AI-Based Approaches for X-ray Image Interpretation

Radiologists have a significant opportunity to raise the standard of care and highlight the importance of radiography in patient care and public health through the use of artificial intelligence.Given that radiographs are the most common imaging tests carried out in the majority of radiology departments, the potential for AI to assist in the triage and interpretation of conventional radiographs (X-ray images) is especially noteworthy.The development of AI algorithms for the interpretation of chest and musculoskeletal (MSK) radiographs has advanced significantly in recent years, with deep learning currently holding a leading position in picture analysis.Compiling large public and private image data sets has facilitated the development of AI algorithms for radiograph interpretation; many of these algorithms show accuracy comparable to radiologists for targeted, targeted tasks.The foundation for current AI solutions to support chest and MSK radiograph triage and interpretation; opportunities for AI to support non interpretive tasks related to radiographs; and considerations for radiology practices choosing AI solutions for radiograph analysis and integrating them into current IT systems.While all-encompassing AI solutions spanning modalities are still in the early stages of development, organizations may start choosing and implementing targeted solutions that boost productivity, improve quality and patient safety, and provide value for their patients.