OpenEvidence, an AI-enabled medical research aggregate platform for doctors, has closed a Series A funding round with Sequoia Capital, boosting its valuation to $1 billion and bringing its total raise to more than $100 million. 

WHAT IT DOES 

OpenEvidence is a free medical information platform that offers an AI-enabled medical copilot for doctors in the U.S. It aggregates and synthesizes clinical evidence to help doctors make more evidence-based decisions. 

The company's AI is trained on specialized content through strategic collaborations, including a newly announced partnership with The New England Journal of Medicine. 

Through the partnership, all NEJM content from 1990 forward will be provided to OpenEvidence to help inform the answers provided to providers on its platform.  

OpenEvidence was launched from the Mayo Clinic Platform Accelerator program.

The funds will be used to train its next-generation LLMs and grow its team of scientists working at the intersection of LLMs and medicine. It will also use the investment to develop strategic content partnerships and work with medical researchers to grow its library. 

"As we come upon our platform's two-year anniversary later this spring, OpenEvidence is trusted and used daily by hundreds of thousands of doctors. But we're just getting started," Daniel Nadler, founder of OpenEvidence, said in a statement.

"Our Series A with Sequoia will enable OpenEvidence to continue building the most trusted AI platform for doctors and other medical professionals in the world."

MARKET SNAPSHOT

In 2023, a study revealed that OpenEvidence's AI scored over 90% on the United States Medical Licensing Examination (USMLE), making 77% fewer errors than ChatGPT, 24% fewer than GPT-4 and 31% fewer errors than Google's Med-PaLM 2.  

Last year, Saurabh Gombar, adjunct faculty at Stanford Healthcare and chief medical officer at Atropos Health, joined HIMSS TV to discuss a study that analyzed the accuracy and efficacy of five LLMs, including OpenEvidence and ChatRWD.

"OpenEvidence and ChatRWD were able to produce actionable, reliable evidence either 42% or 60% of the time – an entire magnitude greater than the general purpose LLMs," Gombar said.