Elsevier unveils Embase AI to transform biomedical data research
The tool has been developed in collaboration with the scientific community and is built upon Elsevier's Embase platform, a widely used biomedical literature database. According to feedback from beta users, Embase AI can reduce the time spent on reviewing biomedical data by as much as 50%.
Natural language features
Among its central features, Embase AI allows users to conduct searches in natural language, ranging from basic to complex scientific queries. The system then provides instant summaries of the relevant data and research insights. Each answer comes with a list of linked citations to assist users in evaluating the evidence and meeting expectations around medical regulation.
Unlike some other AI solutions that may obscure data provenance, Embase AI delivers transparency by presenting citations and ensuring that the underlying sources can be cross-checked. The database underpinning Embase AI is updated continuously and includes records such as adverse drug reaction reports, peer-reviewed journal articles, and around 500,000 clinical trial listings from ClinicalTrials.gov. This makes it suitable for a range of professional needs, including medical research, pharmacovigilance, regulatory submissions and the generation of market insights.
Expanded access
By enabling natural language querying, Embase AI seeks to open up biomedical data analysis to a broader group of users, including those who may lack advanced technical experience with literature reviews. Information is summarised for swift consumption while retaining the supporting references, limiting the likelihood that important findings go overlooked.
The AI solution uses a dual-stage ranking system to generate summary responses with inline citations. This approach is designed to ensure transparency and help users trust the results. A human-curated hierarchy of medical concepts and their synonyms underpins the system, contributing to the precision and transparency of its outputs. Embase AI's records are updated daily, and its architecture allows the tool to function in real time, searching the platform's full content including peer-reviewed research, clinical trials, preprints and conference abstracts.
Security and privacy
Elsevier has stated that Embase AI was developed in accordance with its Responsible AI Principles and Privacy Principles to ensure robust data privacy and security. The company notes that the model's use of third-party large language models (LLMs) is private, with no user information being stored or employed to train public versions of these models. All data is retained solely within Elsevier's protected environment. "Embase AI is changing the way researchers and other users go about solving problems and helps them save valuable time searching for answers, digesting information, and avoiding the risk of missing valuable insights. Every user should have access to trusted research tools that help them advance human progress, and we remain committed to working in partnership with scientists across academia, life sciences and other innovative industries to ensure that our solutions address their needs. We know that our users seek solutions that they can trust, and we built Embase AI in a way that ensures transparency, explainability and accuracy."
This statement was made by Mirit Eldor, Managing Director, Life Sciences at Elsevier.
Ongoing development
Embase AI is the latest addition to Elsevier's suite of products aimed at supporting the biomedical research community by facilitating discovery, analysis, and evidence synthesis using responsibly developed AI tools underpinned by trusted content. The platform is designed to meet the needs of professionals in roles such as research and development, medical affairs, academic research, knowledge management, and medical education.
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