OpenEvidence’s Meteoric Rise Is Huge For Doctors

OpenEvidence has raised hundreds of millions of dollars. In its most recent funding round, it raised a $210 million Series B from prominent investors such as Google Ventures and Kleiner Perkins, bringing the company’s valuation to nearly $3.5 billion.

The company’s goal is straightforward and incredibly ambitious: to collate the entire corpus of medical knowledge and research developments in a way that is easily accessible to physicians and thereby, improve health outcomes.

The service is aiming to provide a similar “touch and feel” as other generative AI services such as Chat GPT or Google’s Gemini—except, its target audience is primarily doctors. Medical science is a rapidly evolving field with a perpetually expanding sea of knowledge, especially as research and development across the fields of disease, therapeutics and human sciences continue to grow. In fact, as Sequoia Capital notes, “a new PubMed article [which is often the flagship resource for peer-reviewed science studies]

is published every three minutes, and medical knowledge doubles every 73 days,” meaning that there are always opportunities to learn and explore new ways to treat. OpenEvidence, now having been trained on nearly 35 million peer reviewed publications, is aiming to automate these opportunities by democratizing the search for information. Using a chatbot interface, physicians can query millions of studies to understand the latest research, trends and diagnostic tools available for a given condition or situation; according to the company, the service is already being used by more than 40% of physicians in the United States across 10,000 hospitals and medical centers.

The value of this type of technology is increasingly being recognized. The latest models for OpenAI’s ChatGPT (GPT 4.1 and o3) have displayed incredible efficacy in taking command of medical knowledge; in May, the company published its work with HealthBench, proposing a rubric for model performance in healthcare and also indicating that GPT’s latest models performed at par or even better than standard physician evaluations. Even Google’s Gemini family of models has made significant progress in this space; its MedLM suite, for example, is a highly tuned model that can aid the entire healthcare workflow, ranging from answering medical questions to deciphering unstructured health data.

Why is all of this important?

There are a few different reasons. First and foremost, this technology is aiming to democratize medical knowledge in a way that is easy to access. Furthermore, it comes at a time when the healthcare system, and its respective workforce, is facing unprecedented headwinds. Studies have repeatedly indicated that physician burnout and attrition are incredibly concerning problems for health systems and organizations of all sizes; physicians simply do not have the bandwidth to fulfill all of their patient care duties in addition to the increasingly prevalent administrative, compliance and regulatory burdens placed on them. This also means that there is less time for professional development and continuing education.

These technologies can serve as a major advantage to the physician workflow as they provide an opportunity to easily query, fact-check and understand the latest science that is involved with a condition. Carry this even further with tools such as OpenEvidence DeepConsult, which gives physicians access to PhD-level AI agents that can conduct medical research, or Gemini’s foundation models that can rapidly decipher medical images, or even AI scribing technology that can rapidly generate patient-physician encounter notes, and soon, hours can be saved from a physician’s daily workflow.

This translates not only to millions of dollars saved annually in system costs, but also to more time available to spend with patients, improved access to care, and ultimately, increased efficacy and quality of care provided.