AI-Based Cancer Diagnosis System Eliminates Errors

The first-ever artificial intelligence (AI)-based digital pathology diagnostic system has been deployed in a live clinical setting, following a pilot period, in which the system identified isolated major errors in retrospective prostate core needle biopsies (PCNBs) that had been diagnosed as benign.

The Second Read (SR) system, developed by Ibex Medical Analytics (Tel Aviv, Israel), a developer of AI-powered cancer diagnostics, has been deployed at the pathology institute of Maccabi Healthcare Services, which is among the largest healthcare providers in Israel and is also the company’s strategic partner. The lab is a centralized pathology institute that handles 160,000 histology accessions per year, out of which approximately 700 are PCNBs.


Ibex develops AI-driven clinical decision support tools that help pathologists deliver more efficient, metric-driven, objective and accurate diagnosis. The company combines AI, data science, image analysis and machine learning technologies and applies them to cancer diagnostics in digital pathology. The SR system is software that identifies various cell types and features within whole slide images of PCNBs, including grading of cancerous glands and other clinically significant features. Ibex's algorithm utilizes state-of-the-art AI and machine learning techniques, and has been trained on thousands of image samples taken from hundreds of PCNBs from multiple institutes.

"We are excited to be the first company to ever deploy an AI-based system in a clinically-active pathology lab, leveraging the enormous potential of AI to make a real impact on human lives. We are now putting our full focus on making this system commercially available," stated Joseph Mossel, CEO and Co-founder of Ibex Medical Analytics.

"The complexity of prostate cancer diagnosis, together with the considerable shortage of pathologists, makes a second read system like this extremely useful for diagnostic accuracy and safety," said Dr. Judith Sandbank, Head of the Maccabi pathology institute and Chief Medical Officer at Ibex Medical Analytics.

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Ibex Medical Analytics