Elsevier, a global leader in scientific information and analytics, has introduced Embase AI, a generative AI–enabled extension of its flagship biomedical literature database, Embase. The platform is designed to simplify the process of discovering, analyzing, and synthesizing insights from biomedical literature, clinical trials, and conference proceedings, serving a wide range of professionals, including researchers, medical affairs teams, product managers, R&D, knowledge managers and educators.
Developed in partnership with the research community, Embase AI was built on responsible and privacy-conscious AI frameworks. It enhances literature retrieval with natural language query capabilities, enabling users to pose research questions conversationally and receive concise, summarized answers with full citations. The tool draws on Embase’s 50-year corpus—updated daily and encompassing peer-reviewed studies, preprints, clinical trials, and conference abstracts—and applies a two-stage ranking process combined with a curated medical thesaurus to ensure transparency, precision, and explainability.
The design of Embase AI focuses on democratizing access to biomedical insights, reducing barriers for those without technical expertise, and accelerating decision-making by presenting key references upfront. To protect user and source data, Embase AI employs internal large language models within Elsevier’s secure environment, ensuring that no third-party models are trained on private inputs.
The introduction of Embase AI follows Elsevier’s commitment to developing AI-driven research tools guided by ethical principles. This new tool further builds on several partnerships between Elsevier and the scientific community to advance trusted and equitable access to research.
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