science

OpenEvidence

OpenEvidence is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand OpenEvidence rather than just read about it. In short: OpenEvidence is the name for both the product (an LLM) and the company, an American artificial intelligence company that develops a medical search engine, which is used by healthcare professionals for clinical decision making support. The company was founded in 2022 by entrepreneur Daniel Nadler and is headquartered in Miami, Florida.

Key takeaways

OpenEvidence belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect OpenEvidence to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of OpenEvidence from memory before moving on to harder problems.

Reference excerpt

OpenEvidence is the name for both the product (an LLM) and the company, an American artificial intelligence company that develops a medical search engine, which is used by healthcare professionals for clinical decision making support. The company was founded in 2022 by entrepreneur Daniel Nadler and is headquartered in Miami, Florida. As of January 2026, the company is valued at $12 billion, following several funding rounds.

History OpenEvidence was founded in 2022 by Daniel Nadler, a Harvard Ph.D. and former founder of Kensho, a financial analytics firm acquired by S&P Global in 2018. Nadler launched the company with co-founder Zack Ziegler, a machine learning researcher from Harvard, to address the challenge physicians face in keeping up with the growing volume of medical literature. In 2023, OpenEvidence reported that its artificial intelligence model achieved a 90 percent score on the United States Medical Licensing Examination (USMLE). During the same year, the company participated in the Mayo Clinic health-technology accelerator. In 2025, the company stated that the model had reached a 100 percent score on the same examination. In February 2025, OpenEvidence raised $75 million in a Series A round led by Sequoia Capital, valuing the company at $1 billion. In July 2025, the company secured $210 million in funding from a round led by GV (Google Ventures) and Kleiner Perkins, with participation from Coatue, Conviction, and Thrive Capital. This round valued the company at $3.5 billion. In April 2025, OpenEvidence was featured in the Forbes AI 50 list. OpenEvidence has established content licensing partnerships with several medical organizations and journals, including American Medical Association, National Comprehensive Cancer Network (NCCN), American Academy of Family Physicians, American College of Emergency Physicians, New England Journal of Medicine, Journal of the American Medical Association, JAMA Network, JAMA Oncology, and JAMA Neurology. On October 20th 2025, the company announced a US$200 million Series C funding round, valuing the company at US$6 billion. As of December 2025, the company reported 760,000 registered U.S. physicians and approximately 18 million clinical consultations per month. In January 2026, OpenEvidence raised $250 million in a Series D funding round at a $12 billion valuation. As of early 2026, OpenEvidence had raised approximately $700 million in total funding from investors including Google Ventures, Nvidia, Sequoia, Blackstone, Thrive Capital, Kleiner Perkins, Craft Ventures and Mayo Clinic.

Partnerships In May 2026, OpenEvidence partnered with Cedars-Sinai to integrate its decision support platform into the hospital's electronic health record infrastructure. The integration allows healthcare professionals to query medical literature using patient-specific clinical context.

Product and technology OpenEvidence provides a Large Language Model with an artificial intelligence-based platform that analyzes and organizes peer-reviewed medical literature from clinical journals such as The New England Journal of Medicine and The Journal of the American Medical Association. According to the company, access is available at no cost to verified physicians, with revenue derived from advertising. As of July 2025, the company reported more than 430,000 registered U.S. physicians (about 40% of U.S. physicians) and use in over 8.5 million consultations per month. In the same month, OpenEvidence introduced a feature called DeepConsult, described as employing reasoning models to synthesize findings across multiple studies. In May 2026, the company introduced Voice Mode, a voice-based interface for its platform that was made available through the company's web and mobile applications. Practitioners can utilize the platform with their National Practitioner Identity numbers to create an account free of charge. When prompting an inquiry OpenEvidence will then begin referencing published medical journals to provide seemingly up to date information, and suggestion on clinical topics.

Recognition In 2025, Daniel Nadler, co-founder of OpenEvidence, was named to the TIME100 Health list of the 100 most influential people in global health.

See also Clinical decision support system Generative artificial intelligence Large Language Models (LLMs) List of open-source health software List of freeware health software

References

External links Official website

Illustrations

Worked examples

Example 1 — a first encounter with OpenEvidence

Start with the simplest possible case. Write down what OpenEvidence claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to OpenEvidence before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about OpenEvidence ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of OpenEvidence

In research: OpenEvidence appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses OpenEvidence in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: OpenEvidence is common in secondary-school and first-year university syllabi. It links to neighbouring topics 2022 in artificial intelligence, American companies established in 2021, Artificial intelligence companies, so understanding it makes those chapters shorter.
In everyday life: Look for OpenEvidence outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study OpenEvidence in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what OpenEvidence means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain OpenEvidence out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is OpenEvidence in simple terms?

Why does OpenEvidence matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study OpenEvidence?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on OpenEvidence.