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

• URL: http://arxiv.org/abs/2310.16146v1
• Date: Tue, 24 Oct 2023 19:43:39 GMT
• Title: Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature
• Authors: Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang, and Nigam Shah
• Abstract summary: We release Clinfo.ai, an open-source WebApp that answers clinical questions based on dynamically retrieved scientific literature. We report benchmark results for Clinfo.ai and other publicly available OpenQA systems on PubMedRS-200.
• Score: 44.715854387549605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source summarization tools based on large language models (LLMs) now exist, rigorous and systematic evaluations of their outputs are lacking. Furthermore, there is a paucity of high-quality datasets and appropriate benchmark tasks with which to evaluate these tools. We address these issues with four contributions: we release Clinfo.ai, an open-source WebApp that answers clinical questions based on dynamically retrieved scientific literature; we specify an information retrieval and abstractive summarization task to evaluate the performance of such retrieval-augmented LLM systems; we release a dataset of 200 questions and corresponding answers derived from published systematic reviews, which we name PubMed Retrieval and Synthesis (PubMedRS-200); and report benchmark results for Clinfo.ai and other publicly available OpenQA systems on PubMedRS-200.

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