How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?

AZmed, one of the leading AI radiology companies, is introducing RIS-integrated report prefill that sends structured findings directly from the Rayvolve® AI Suite into EDL and Evolucare systems. The report templates are where radiologists already work, waiting to be reviewed, amended, and signed.

Rayvolve® has four modules that cover high-volume use cases. AZtrauma is used for suspected osseous fractures, joint dislocations, and assessment of joint effusions, whereas AZchest encompasses thoracic abnormalities such as pulmonary nodules, pneumothorax, and pleural effusion. AZmeasure automatically produces linear and angular MSK measurements. AZboneage calculates pediatric bone age based on the Greulich & Pyle method and performs skeletal maturity assessment per the Greulich & Pyle atlas. For both pediatric and adult workflows, algorithmic inference time is < 60 s per study.

The radiologist’s role in 2025 is no longer limited to just reading films. They’re expected to manage skyrocketing imaging volumes, advise multidisciplinary teams, and provide fast, error-free reports under constant medico-legal scrutiny. As a result, healthcare centers are choosing to adopt AI tools that help radiologists spend less time on repetitive tasks and more time on high-level interpretation, clinical decision-making, and direct collaboration with other specialists.

In this blog, we’ll cover how this adoption has changed the role of radiologists, concerns regarding the use of AI in healthcare centers, and how the right tools, like AZmed’s Rayvolve® AI Suite, make this transition easier. But first, let's take a deeper look at how the role of radiologists has changed.

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