OpenEvidence hits $15B valuation as doctors adopt AI

Founded in 2022 by Daniel Nadler, Miami-based OpenEvidence has captured significant traction in medical settings. The startup closed a $250 million funding round in September at a $15 billion valuation, bringing its total raised capital to nearly $1 billion over 18 months from investors including Andreessen Horowitz, Blackstone, Thrive Capital, and Sequoia. Supported by an advertising model, OpenEvidence booked $100 million in ad buys for 2026 by late 2025 and projects generating between $400 million and $500 million from ads by the end of 2026.

The system sees heavy daily use, processing over 42 million prompts last month as nearly two-thirds of America's 1 million physicians turn to it for decision support. OpenEvidence indexes literature from sources like the New England Journal of Medicine and the Journal of the American Medical Association, and has integrated with electronic health records at Mount Sinai Health System, Memorial Sloan Kettering Cancer Center, and NYU Langone. The company is broadening into ambient note-taking, clinical trials for cancer and rare diseases, and literature-based drug discovery. To bolster these efforts, it hired Alexander Tarashansky, creator of Anthropic's Claude Science, and partnered with Anthropic to extend healthcare tools to 100 low- and middle-income countries.

However, OpenEvidence faces growing competition and technical scrutiny. Competitors include OpenAI's ChatGPT for healthcare, Google's Gemini, and Wolters Kluwer, which launched an AI-powered version of its UpToDate software in October 2025. Evaluation results remain mixed: a Mayo Clinic study showed accurate recommendations across 5 sampled case studies, but a June paper in Nature found OpenEvidence underperformed general models like GPT and Gemini on clinical benchmarks. Concerns also remain over junior doctors relying on AI, while regulatory hurdles under the EU AI Act led the startup to exit the EU and UK.

For clinicians, OpenEvidence drastically speeds up evidence retrieval during patient care. Yet practitioners like NYU Langone neurosurgeon Eric Oermann emphasize that sparse guidance exists for clinical implementation, making careful human oversight essential as AI tools expand deeper into hospital workflows.