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AI Case Study

Enlicit classifies malignant tumours with 50% better accuracy than humans and 0 false-negatives with deep learning

Enlitic, a pioneer in medical deep learning, is leveraging AI to help radiologists in identifying diseases and medical conditions like cancers and tumours with greater accuracy and speed. When tested, Enlitic's system achieved 50% greater accuracy than hree expert human radiologists in collaboration, and 0% false-negative rate, compared with 7% for practiocioners.



Healthcare Providers And Services

Project Overview

"In another ongoing clinical study that Lyman hopes will be accepted for publication in late 2018, Enlitic’s AI model is capable of finding malignancies in low-dose chest CT lung cancer screening studies up to two years earlier than radiologists." (Enletic)

Reported Results

In a test against three expert human radiologists working together, Enlitic's system was 50% better at classifying malignant tumours and had false-negative rate (where cancer is missed) of zero, commpared with 7% for humans (Economist).


"Enlitic works with a wide range of partners and data sources to develop state-of-the-art clinical decision support products.

Our deep learning technology can incorporate a wide range of unstructured medical data, including radiology and pathology images, laboratory results such as blood tests and EKGs, genomics, patient histories, and electronic health records (EHRs). This richness allows higher accuracy and deeper insights for every patient.

Our solutions integrate seamlessly into your existing health system infrastructure. For example, our radiology solutions communicate with third party image viewers and archiving systems."


R And D

Core Research And Development


"Enlitic (San Francisco, CA) is developing a deep learning tool for radiologists that augments their reading and interpretation. Last May, the company won the top prize of €1 million at the rst Cube Tech Fair in Berlin, Germany.

“We are developing AI with the goal to cover 95% of diagnostic radiology by 2020,” says Kevin Lyman, COO at Enlitic. The company’s focus, he explains, is to enhance radiologists’ efficiency, pro ciency, and more importantly, accuracy.

There are three different ways that Enlitic sees the potential for deep learning AI in radiology. One is to perform quality assessments, or a second read, after initial interpretation on the images and the report. Two, to triage incoming studies in order to prioritize and appropri- ately route through the organization. Three, to deliver real-time diagnostic support alongside a radiologist." (

"The firm's technology is currently being tested in 40 clinics across Australia (Economist)."




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