Transforming Healthcare: OpenEvidence, the AI Startup Keeping Medical Professionals Informed

JJohn July 27, 2023 1:52 PM

OpenEvidence, an artificial intelligence startup, is challenging the limitations of AI by creating a chatbot that integrates real-time scientific research data, aiming to keep medical professionals up-to-date with the latest findings. Backed by a robust team and significant funding, the company's model focuses on the 'retrieval augmented generation' process, providing new data right before answering queries.

Unfreezing AI with real-time data

Standard large language models, an AI that generates human-like text based on the input it receives, have a significant limitation: they're frozen in time. Once trained, they rely on the data available at that point and can't update themselves with real-time information. But not OpenEvidence. This new player in the AI field is designed to deal with this problem by continuously updating its AI with the latest findings from clinical documents, providing a real-time flow of information. With this approach, the chatbot can provide more current and relevant responses to medical queries, a significant leap forward in the functionality and potential of AI in medicine.

OpenEvidence isn't Daniel Nadler's first foray into the world of AI startups. He previously founded Kensho Technologies, an AI tool for Wall Street traders that analyzes millions of market data points. S&P Global bought Kensho for a whopping $550 million (plus $150 million in stock) back in 2018. This successful exit demonstrates Nadler's significant experience and understanding of the AI field, setting a promising groundwork for OpenEvidence.

Challenging the giants of healthcare databases

Nadler's OpenEvidence has set its sights on competing with UpToDate, a widely used database among healthcare workers worldwide. UpToDate, owned by Netherlands-based data company Wolters Kluwer, relies on more than 7,000 human experts to write and edit its entries on medical topics. OpenEvidence, on the other hand, offers an interactive AI-based solution that can scan tens of thousands of journals and tailor responses to precise patient scenarios. If successful, this could mark a significant shift in how healthcare professionals access and interact with medical data.

Quality control in data retrieval

Not all medical journals are created equal, so OpenEvidence has baked a ranking system into its AI model. Using a system known as 'impact factor', the model treats journals that are more highly cited as more important. This ensures that the information retrieved from the pool of new journal articles is of high quality. In essence, OpenEvidence doesn't just provide 'evidence', but 'evidence weighted answers', ensuring that healthcare professionals can trust the responses they receive.

Early adopters, provided they're licensed medical professionals, can currently use OpenEvidence for free. While the revenue model for OpenEvidence is still being debated, Nadler is evaluating a hybrid model with a combination of ad-based revenues and subscription upsells. Regardless of its eventual monetization strategy, OpenEvidence's primary goal remains clear: to provide a tool that assists doctors and nurses in accessing the latest medical research, enhancing their decision-making capabilities.

More articles

Also read

Here are some interesting articles on other sites from our network.