Development of Intelligent Representative Paper Identification Tool: System Architecture, Core Functions, and Key Technologies

Corresponding author: LIU Hui, liuhui@pumc.eud.cn

DOI: 10.12201/bmr.202608.00013

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  Abstract: Purpose/Significance To develop intelligent identification tool for representative papers, addressing the practical needs for identifying and evaluating representative work, and providing technological support for related scientific evaluation and research management practices. Method/Process Based on the conceptual proposition of combing intelligence analysis methodologies and artificial intelligence (AI) techniques, an architecture with biomedical literature as the core built-in data is constructed, comprising three hierarchical layers: data management, content mining, and intelligent services. Typical representative paper identification methods are selected and automated. Intelligent question-answering and automated review generation are implemented based on context engineering. Result/Conclusion The tool employs representative paper identification methods as its algorithmic core, ensuring that the identification process is traceable and the results are verifiable. Concurrently, it incorporates multiple AI techniques to enhance its intelligent functionality and user-friendliness, thereby achieving the engineered application of AI technologies within intelligence analysis workflows.

  Key words: representative papers; intelligence analysis; context engineering; artificial intelligence; scientific creativity

  Submit time: 5 August 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2026-05-15	10.12201/bmr.202608.00013V1	Download

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