Enhancing Biomedical Literature Retrieval with Level of Evidence and Bio-Concepts: A Comparative User Study

Abstract

Most traditional medical search engines rely on similarity-based retrieval between the user’s query and document content, ignoring important contextual aspects, such as the quality of evidence and specific biomedical concepts. Addressing these limitations, we developed WisPerMed, a medical search engine that enhances search efficiency by incorporating Level of Evidence (LoE) and bio-concepts into both the ranking process and the user interface. This study compares the performance of WisPerMed to PubMed through a user study involving 131 medical experts. Our findings indicate that WisPerMed significantly reduces the number of queries needed and the time spent on searches, with large effect sizes compared to PubMed (0.822 and 0.551, respectively). The integration of LoE and bio-concepts not only improves the ranking of relevant and authoritative articles but also facilitates their identification through enhanced GUI elements. These results highlight the potential of WisPerMed to enhance search efficiency in clinical and research settings, ultimately supporting faster and more accurate access to medical knowledge.

Sameh Frihat

Researcher in the first cohort

My research interests include Information Retrieval, Natural Language Processing, Machine Learning, and Explainable AI.

Norbert Fuhr

Principal Investigator

My research interests include Information Retrieval, Natural Language Processing and Computer Science.