AI-Enhanced Biomedical Literature Retrieval via Large Language Models and Researcher Preference-Aware Semantic Matching
Keywords:
biomedical literature retrieval, large language models, semantic matching, researcher preference modeling, personalization, system architecture, fairness, governanceAbstract
The exponential growth of biomedical literature imposes significant cognitive and operational burdens on researchers who must navigate millions of articles to locate information both specific and serendipitously relevant. Traditional keyword-based search engines, while foundational, often fail to capture the nuanced conceptual interrelations embedded in scientific prose, leading to retrieval results that are poorly aligned with the latent intent of domain experts. This paper presents a comprehensive architectural and systems-level analysis of an AI-enhanced biomedical literature retrieval framework that integrates large language models with a researcher preference-aware semantic matching engine. Rather than proposing a singular algorithm, we examine the design, deployment, governance, and sustainability of a full-stack system that leverages the generative and representational capacities of transformer-based models to construct dynamic user profiles, refine queries through multi-turn interactions, and re-rank candidates using cross-encoder architectures informed by individual research trajectories. We discuss structural trade-offs between latency, precision, and personalization, and investigate how decentralized model fine-tuning, federated preference aggregation, and modular pipeline designs can address robustness and fairness challenges in high-stakes biomedical contexts. The analysis extends to infrastructure considerations, including model compression, edge-cloud orchestration, and energy-aware scheduling, as well as policy dimensions such as data privacy, algorithmic transparency, and the mitigation of confirmation bias that arises from over-personalization. By framing retrieval not as a transactional process but as a socio-technical dialogue between scientist and knowledge base, this paper outlines a principled pathway toward next-generation literature systems that sustain rigorous scientific discovery while remaining ethically grounded and operationally viable.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
