As a referral centre for thoracic cancer, Sant’Andrea University Hospital in Rome, Italy, manages a significant imaging workload – interpreting over 20,000 chest X-rays every year. With growing demand and limited time, the need for intelligent support became clear.

Addressing the need for AI in radiology

At Annalise.ai, we’re focused on providing solutions that tackle specific challenges. We spoke with Professor Andrea Laghi, Chairman of Radiology at Sant’Andrea, to hear his early impressions of our comprehensive and customisable AI solution. Here’s what he had to say about how Annalise is already making a difference, highlighting the need for AI in healthcare to assist with routine workflows and improve efficiency.

“We have a large demand for chest X-rays… we need a solution [for] helping our radiologists in reducing the workload.”

Professor Andrea Laghi explains why he chose Annalise Enterprise CXR.

Why Annalise.ai?

Despite being a referral centre for thoracic cancer, when selecting the right AI solution, Professor Laghi and his team were focused on finding a decision-support AI solution that identified more than a single or limited set of findings.  They were seeking a comprehensive solution capable of supporting a broad spectrum of diagnostic needs.

“I didn’t want a software making a simple, single task… I want a more comprehensive software giving us an overall evaluation of the different findings in a chest X-ray.”

Annalise Enterprise CXR’s ability to identify up to 124 different findings, including trauma, incidental findings and technical factors, made it the ideal choice for Sant’Andrea Hospital.

The Annalise.ai solution is exactly what we were looking for… reporting more than a hundred different findings.

Early impact and positive feedback

Within the first few weeks of deployment, the AI tool had already received positive feedback from clinical users, particularly for its ability to aid in the interpretation of complex post-operative chest X-rays – often some of the most challenging to interpret. These scans must distinguish between normal post-surgical changes, potential complications, and incidental findings while balancing their limited diagnostic value against associated risks. Proper interpretation demands both clinical context and expertise. As Professor Laghi shared:

Our most important positive feedback at the moment is in the post-operative chest X-ray, which are often quite difficult to interpret… Our radiologists now like Annalise.ai, especially in this setting.

The future of AI in radiology and healthcare

Looking ahead, Professor Laghi is confident that AI in healthcare diagnostics will continue to evolve, becoming an essential tool for radiologists facing increasing demand and limited resources.

“AI is a part of the healthcare and in particular of the radiological world… We need to maximise efficiency in our work while preserving quality. We need support from outside.”

At Annalise.ai, we’re proud to be part of this transformation, offering scalable AI solutions that enhance medical imaging analysis and improve healthcare delivery worldwide.

Ready to streamline your radiology workflow? Let’s talk. Contact us here.