As Digital Breast Tomosynthesis (DBT) increasingly becomes the standard of care for mammographic imaging, stakeholders say there is a need for algorithms to optimize reading efficiency while maintaining or improving the accuracy achieved with DBT.

While so much of AI’s promise for healthcare lies in the future, one need not look too far to see how much it’s a concrete reality right now. 

Given the abundance of digital healthcare data currently available, properly organizing it has become a top priority for modern researchers seeking to use it to enhance research efforts using new AI.

Among other things, say two stakeholders, by taking over routine tasks, adding quality checks, and enhancing diagnostic accuracy, AI algorithms can be expected to improve clinical outcomes.

Despite numerous barriers, the use of AI in medical imaging holds a lot of promise, but industry stakeholders need to work together to ensure the technology is safe, effective, and efficient.

AI is poised to make profound impact on radiologists’ efforts, assisting human experts with computer-powered algorithms to enhance interpretation and improve classification of medical imaging results.

Thanks to new open-source AI that runs inside a web browser, patients can begin to get do-it-yourself X-ray diagnoses.

Unlike previous studies, which have investigated the potential of NLP and computer vision techniques in the classification of radiographs, researchers focused on prospects of using AI for real-time prioritization.

Use of artificial intelligence in radiology isn't a fleeting trend, say analysts, as it’s likely to be the norm within just a few years.