A chest x-ray AI decision support tool for radiologists and clinicians that detects 124 clinical findings has been launched. The study found that when used as an assist device, Annalise CXR significantly improved the ability of radiologists to perceive 102 chest x-ray findings in a non-clinical environment, was statistically non-inferior for 19 findings and no findings showed a decrease in accuracy. It also assessed the stand-alone performance of the model in a non-clinical environment against radiologists in identifying chest x-ray pathology, as well as investigating the effect of model output on radiologist performance when used as an assist device. Annalise CXR's AI model classification alone was said to be significantly more accurate than unassisted radiologists for 117 of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings. Co-founder and ceo Dimitry Tran said: "A major challenge facing global health systems is that the number of scans requiring clinical interpretation is growing at a greater pace than increases in the number of radiologists to interpret them.

A new deep learning model could help radiologists in any facility interpret chest X-rays. In a new study published in The Lancet Digital Health, investigators from Australia outlined their new tool. It is designed to alleviate heavy workloads and make it easier for providers who do not have specialty thoracic training to read these scans while reducing errors. Chest X-rays are already the most common imaging study worldwide, and that number is growing, said the team from annalise.ai, the company that created the AI model. Developing a tool to help shoulder the weight and process the workload will be critical. "The ability of the AI model to identify findings on chest X-rays is very encouraging," said Catherine Jones, MBBS, thoracic radiologist, chest lead at annalise.ai,

The company was originally formed as a joint venture between Australian healthcare technology company Harrison.ai The launch of Annalise CXR coincides with its publication of a peer-reviewed diagnostic accuracy study published by The Lancet Digital Health, which is the largest of its kind ever undertaken in terms of the number of findings concurrently evaluated. The study found that when used as an assist device, Annalise CXR significantly improved the ability for radiologists to perceive 102 chest X-ray (CXR) findings in a non-clinical environment, was statistically non-inferior for 19 findings and no findings showed a decrease in accuracy. The study also assessed the standalone performance of the model in a non-clinical environment against radiologists in identifying chest x-ray pathology, as well as investigating the effect of model output on radiologist performance when used as an assist device. Annalise CXR's AI model classification alone was significantly more accurate than unassisted radiologists for 117 (94%) of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings.