A study by the Centre for Advanced Research in Imaging, Neuroscience and Genomics (CARING) found that AI powered CAD software can reduce hedging and defensive reporting statements in radiologist reports, resulting in clearer more actionable diagnosis descriptions. The study was undertaken using Oxipit ChestEye AI imaging suite. Moreover, the study found that nearly 80% of the reports generated by Oxipit ChestEye were as accurate as the radiologists’ reports and were more accurate in 5% of analyzed patient cases.

The study led by Dr Vasanth Venugopal aimed to identify elements of hedging in clinical radiology reports and evaluate the accuracy and clinical validity of AI-generated reports of chest X-rays. A set of 297 retrospective chest X-ray images was used in the study. The images were analyzed by Oxipit ChestEye and AI generated reports compared to the ones produced by a radiologist. Final report comparison and diagnosis validation was conducted by a radiologist with 9 years of clinical experience. The full study titled “Judging the Accuracy and Clinical Validity of Deep Learning-Generated Test Reports” will be presented at the RSNA conference in Chicago later this year.

“Subjectivity is part of human nature. Sometimes radiologists might be fairly confident that there is no pathology in the analyzed case, but they choose to include inconclusive vague diagnosis statements to hedge against potential mistakes or legal threats. On the contrary, AI produces clear, unbiased reports that can help to reassure radiologist in their opinion. A combination of both produces clearer, actionable reports to make treatment decisions, as well as helps to avoid unnecessary procedures resulting from evasive and non-committal reports” – notes Oxipit’ Chief Medical Officer Dr Naglis Ramanauskas.

Oxipit ChestEye imaging suite encompasses a fully automatic computer aided diagnosis (CAD) platform which supports 75 radiological findings, covering over 90% of radiological cases presented to radiologists on a daily basis. ChestEye produces a standardized preliminary text report that incorporates all the radiologically relevant information present in a chest X-ray image, speeding up case description and minimizing the risk of overlooked secondary findings. In February Oxipit ChestEye received CE certification. Internal platform trials showed 30% time saved per patient case and reduced error rate by up to 50%.

“Oxipit ChestEye was designed as a productivity and second-opinion tool to aid the work of radiologists. The evaluation that nearly 80% of preliminary reports generated by our software were clinically accurate to be deemed as final reports, and in 5% cases – even more accurate than the ones produced by a radiologist, is an inspiring validation of current capabilities of AI diagnostics” – notes Dr N. Ramanauskas.

Oxipit ChestEye imaging suite encompasses a fully automatic computer aided diagnosis (CAD) platform which supports 75 radiological findings, covering over 90% of radiological cases presented to radiologists on a daily basis. ChestEye produces a standardized preliminary text report that incorporates all the radiologically relevant information present in a chest X-ray image, speeding up case description and minimizing the risk of overlooked secondary findings. In February Oxipit ChestEye received CE certification. Internal platform trials showed 30% time saved per patient case and reduced error rate by up to 50%.

“Oxipit ChestEye was designed as a productivity and second-opinion tool to aid the work of radiologists. The evaluation that nearly 80% of preliminary reports generated by our software were clinically accurate to be deemed as final reports, and in 5% cases – even more accurate than the ones produced by a radiologist, is an inspiring validation of current capabilities of AI diagnostics” – notes Dr N. Ramanauskas.

In the words of Dr N. Ramanauskas, even two radiologists can have differing opinions over a particular X-ray report.

“Therefore aiming for 100% correlation to radiologist reports is unrealistic. However, with constant improvement of deep learning algorithms and innovations in the medical imaging field, a radiologist working with the help of AI can help to reduce interpersonal subjectivity and greatly increase the quality of the reports while helping the radiologists become more efficient.” – notes Dr N. Ramanauskas.