To maintain clinical reliability, Open Evidence restricts its knowledge base to a 'walled garden.' This includes PubMed abstracts (excluding predatory journals), guidelines, and full-text content from licensed publishers. This prevents the model from citing unreliable internet sources or 'hallucinating' references, building clinician trust.

For decades, keeping documentation updated was a low-priority task. Now, with AI support agents relying on this content as their source of truth, outdated information leads to immediate, tangible failures. This creates the urgent business case to finally solve knowledge decay.

By analyzing millions of queries from clinicians, Open Evidence identifies high-frequency topics. It then cross-references these with its literature database to pinpoint areas that are both clinically relevant and poorly supported by existing evidence, effectively mapping the frontier of medical knowledge and research opportunities.

Contrary to the "data is the new oil" axiom, historical oncology data has a short shelf-life. The continuous evolution of treatments and data-generation technologies means recent, contextual data is far more valuable for training AI models than large, outdated archives.

AI is poised to revolutionize evidence synthesis by automating the grueling, multi-year process of systematic reviews. The ultimate goal is to enable anyone to get an accurate, near-instantaneous summary of the entire body of research on a specific question, effectively creating meta-analysis on demand.

Open Evidence employs a two-step process that distinguishes it from general LLMs. First, its AI, trained by human subspecialists, identifies the most relevant scientific references for a query. Only then is an answer generated from this curated evidence, prioritizing source credibility over speed.

AI tools can be rapidly deployed in areas like regulatory submissions and medical affairs because they augment human work on documents using public data, avoiding the need for massive IT infrastructure projects like data lakes.

Most doctors don't analyze raw studies. They follow clinical guidelines which function as algorithms. These are the output of a massive, underlying effort by researchers to synthesize thousands of trials into "pre-processed evidence" like systematic reviews, making evidence-based care scalable and efficient.

Recognizing that different specialists have unique information needs, Open Evidence develops distinct AI models for each role. For example, a surgeon's query about a procedure can surface peer-reviewed surgical videos—a feature irrelevant to a medical oncologist—thus enhancing the tool's utility through deep specialization.