AI Radiology Startup Agamon Raises $3M To Transform Clinical Text Into Data

Israeli healthcare startup Agamon announced on Monday that it raised $3 million in a seed round led by UK investment firm MMC Ventures, with participation from InHealth Ventures, Seedcamp, and Bayer G4A.

Founded in 2018 by Michal Meiri, CEO, and Omri Sivan, CTO, Agamon says it helps health systems make sense of their unstructured clinical data. Agamon’s AI software trains hospital computers to “read” a doctor’s unique language, a long-standing barrier for healthcare advancements. Using natural language processing (NLP) to make sense of the text in patient records, the system then puts the text summaries into a structured database format.

Based in Tel Aviv with global operations and customers in the US and the UK, Agamon is already working with hospitals such as Philadelphia-based Jefferson Health System, Detroit-based Henry Ford Health System, and others.

“The inability of today’s computers to understand doctors’ unique way of writing has been a major issue. Doctors can correctly describe an ‘infiltrating mass’ without writing the word ‘tumor’ or write ‘bilateral ground glass peripheral opacity’ without explicitly indicating a suspicion for COVID-19” said Meiri.

Agamon plans to use the funding to scale its deployments with more hospitals globally and further train the AI and widen its clinical spectrum.

Agamon is already used at some of the world’s busiest hospitals.

“Agamon has built advanced AI and NLP (natural language processing) that assess the significance of findings extracted from unstructured medical text, starting with radiology reports. This helps us make sure no important clinical recommendation ever falls through the cracks, and will assist with automating timely review of large report volumes”, says Dr. Danie Siegal, vice chair of radiology at Henry Ford Health System in Detroit, Michigan.

Tom Moon, physician and investor at MMC Ventures, said: “We’re excited by Agamon’s ability to automate the interpretation of medical text and therefore enable products such as automatic follow up and patient-friendly reports. Applications at the intersection of machine learning and healthcare data are helping drive much-needed change in our healthcare systems and are a core part of our investment thesis at MMC.”