Quertle LLC, developer of the first artificial intelligence (AI) biomedical big data platform (BioAI™), has released Qinsight™. The product uses BioAI’s neural networks and other AI methods, along with predictive visual analytics, to enable high-efficiency discovery and insight from biomedical literature.
The visual analytics in Qinsight™ provide summarization, discover emerging trends and uncover hidden connections. These capabilities overcome many of the problems of list-oriented results, which make information assimilation nearly impossible.
Qinsight™ covers more than 40 million documents, including searching the full text of more than 10 million. The content includes, essentially, all biomedical and biological journals, patent grants and applications, NIH grant applications, TOXLINE databases, AHRQ treatment protocols, and more, which, along with the AI underpinnings, gives a deeper perspective than that available anywhere else.
“Qinsight™, as the name implies, provides insight from the biomedical and life science literature that previously was elusive,” said Jeff Saffer, Quertle president and CEO. “We expect the combination of artificial intelligence-based discovery, along with intuitive visual analytics, to become critical to the success of the biomedical industry.”
Qinsight™ is available as a software as a service (SaaS) subscription, with both site licenses and individual user licenses available.
ABOUT QUERTLE
Quertle is a biomedical big data analytics company that has integrated artificial intelligence and other modern technologies into its platform to gain unmatched insight from biomedical and biological text content. The platform minimizes the risk of missing critical documents, providing an important resource for companies, university researchers and health care providers. In addition to Qinsight™, Quertle offers custom-built solutions based on the BioAI™ platform. Quertle boasts an extensive client list, which includes users at the top pharmas, research institutions, universities and hospitals. Quertle is a winner of the National Library of Medicine Innovation Challenge Award.