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The quickly-expanding nature of published medical literature makes itchallenging for clinicians and researchers to keep up with and summarizerecent, relevant findings in a timely manner. While several closed-sourcesummarization tools based on large language models (LLMs) now exist, rigorousand systematic evaluations of their outputs are lacking. Furthermore, there isa paucity of high-quality datasets and appropriate benchmark tasks with whichto evaluate these tools. We address these issues with four contributions: werelease Clinfo.ai, an open-source WebApp that answers clinical questions basedon dynamically retrieved scientific literature; we specify an informationretrieval and abstractive summarization task to evaluate the performance ofsuch retrieval-augmented LLM systems; we release a dataset of 200 questions andcorresponding answers derived from published systematic reviews, which we namePubMed Retrieval and Synthesis (PubMedRS-200); and report benchmark results forClinfo.ai and other publicly available OpenQA systems on PubMedRS-200.

Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature