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.

According to executives polled, obstacles to AI implementation include organizational agility and inadequate staffing, not access to appropriate technology.

Describing it as an AI platform designed to enable hospitals to reap more value from technology such as clinical apps on devices, GE introduced Edison at the end of 2018 and will be showing the platform and related tools at its booth.

Healthcare has been slow to implement technology tools that have transformed other areas of commerce, the FDA noted, in part because of the regulation that accompanies medical products.

According to researchers, the collection of increasingly large and well curated medical datasets has enabled AI tools to predict genetic mutations from imaging phenotypes, improving the way patients are treated.

New AI tools, say experts, can help consumers understand and shop for the plans and benefits they need, when they need them.

A new study finds current laws and regulations do not adequately safeguard individuals' confidential health information.

According to stakeholders, future applications of deep learning can extend beyond imaging data to analyze EHRs, pathology and cellular microscopy data to help develop targeted drugs and achieve greater precision in treatments.

Among other things, say experts, AI-driven technologies can help with the delivery of targeted and personalized medicine while simultaneously ensuring seamless communication and high productivity within smart hospitals.