Elsevier introduced Embase AI, a GenAI-powered version of Embase, the leading biomedical literature database. Embase AI transforms literature searches and other tasks carried out by multiple teams, including R&D, medical affairs, product managers, academic researchers, knowledge managers and educators.
Key benefits include easy access for all users regardless of their technical background, finding insights faster, and increasing confidence in decision-making. Embase AI has been built in partnership with the research community to help users more easily discover, analyze and synthesize research using trusted data powered by responsible AI.
Key features of Embase AI include:
- Natural-language querying: Users from novice to expert can ask questions in natural language and receive instant summaries of data and insights. For example, a pharmacologist might ask ‘What antiepileptic drug is best transported over the blood brain barrier?’ and get a list of applicable drugs with a summary of their different properties.
- Transparent sources: Unlike standard AI tools, every answer in Embase AI includes a list of linked citations, helping users evaluate the strength of evidence and meet regulatory expectations.
- Trusted and up-to-date content: Embase AI draws from Elsevier’s continuously-updated database of biomedical literature, including adverse drug reaction reports, journal articles, and approximately 500,000 records from ClinicalTrials.gov – data vital for functions such as medical research, pharmacovigilance, regulatory submissions and market insights.
Grounded on the Embase database, which has been trusted by researchers globally for more than 50 years, Embase AI searches the entire content corpus in real time, including peer-reviewed research, clinical trials, preprints and conference abstracts, to deliver results based on the most recent data available. Embase AI uses a two-stage ranking system to generate a summarized response with inline citations to ensure transparency. The advanced solution, which is updated daily, relies on a (human) curated hierarchy of medical concepts and synonyms, making its results precise and easy to explain.
Embase AI was developed in line with Elsevier's Responsible AI Principles and Privacy Principles to ensure the highest standards of data privacy and security. Embase AI's use of third-party LLMs is private, no information is stored or used to train public models, and all data is stored in a protected and private environment exclusive to Elsevier.
To request a demo or to learn more about Embase AI, go to this site.