Intelligent Semantic Search Engine for Biomedical Literature and Clinical Trials: A Comprehensive Hybrid Retrieval Framework

The exponential growth of biomedical literature and clinical trial data poses significant challenges for healthcare professionals, researchers, and students in efficiently accessing relevant information. As the volume of scientific publications doubles every few years, traditional information retrieval (IR) systems based on exact keyword matching are increasingly inadequate. These legacy systems struggle with the complex, non-standardized vocabulary of medicine, often failing to retrieve relevant documents due to synonymy ("heart attack" vs. "myocardial infarction") or retrieving irrelevant ones due to polysemy. This "vocabulary mismatch" problem creates a critical knowledge gap, potentially delaying evidence-based clinical decision-making and redundant research efforts. This paper presents the design, implementation, and rigorous evaluation of an intelligent semantic search engine that leverages advanced Natural Language Processing (NLP) and deep learning techniques to facilitate the efficient retrieval of biomedical information. The system implements a robust \textbf{Hybrid Search Architecture} that synergizes the precision of sparse lexical retrieval (BM25) with the semantic recall of dense vector retrieval (BioBERT embeddings). This dual-retrieval strategy is further enhanced by a computationally intensive \textbf{Cross-Encoder Reranking} stage, which utilizes a transformer-based model trained on the MS MARCO dataset to re-score the top candidate documents, significantly improving precision at the top ranks (Precision@10). The search engine indexes and processes data from two primary heterogeneous sources: PubMed research articles and ClinicalTrials.gov records, covering 20 major medical domains including COVID-19, Oncology, Diabetes, and Neurology. Currently, the system maintains a unified index of 1,817 documents enriched with comprehensive metadata and 768-dimensional semantic embeddings. The architecture incorporates state-of-the-art transformer-based models, utilizing BioBERT for document understanding and embedding generation, BioBERT-QA for extractive question answering, and a specific cross-encoder model for result reranking. The system is deployed using a robust, scalable microservices architecture, utilizing Elasticsearch for document storage and vector retrieval, Redis for high-performance caching, and PostgreSQL for managing structured relational data. Experimental results demonstrate a Precision@10 of 0.94 and query latency under 200ms, significantly outperforming baseline methods. This comprehensive study details the system architecture, methodology, experimental results, and outlines a roadmap for future enhancements, including Retrieval-Augmented Generation (RAG) and multi-agent conversational interfaces.