PaperPlain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing

要旨

When seeking information not covered in patient-friendly documents, healthcare consumers may turn to the research literature. Reading medical papers, however, can be a challenging experience. To improve access to medical papers, we introduce a novel interactive interface---Paper Plain---with four features enabled by natural language processing: definitions of unfamiliar terms, in-situ plain language section summaries, a collection of key questions that guides readers to answering passages, and plain language summaries of those passages. We evaluate Paper Plain, finding that participants who used Paper Plain had an easier time reading research papers without a loss in paper comprehension compared to those who used a typical PDF reader. Altogether, the study results suggest that guiding readers to relevant passages and providing plain language summaries alongside the original paper content can make reading medical papers easier and give readers more confidence to approach these papers.

著者
Tal August
Allen Institute for AI, Seattle, Washington, United States
Lucy Lu Wang
Allen Institute for AI, Seattle, Washington, United States
Jonathan Bragg
Allen Institute for Artificial Intelligence, Seattle, Washington, United States
Marti Hearst
UC Berkeley, Berkeley, California, United States
Andrew Head
University of Pennsylvania, Philadelphia, Pennsylvania, United States
Kyle Lo
Allen Institute for Artificial Intelligence, Seattle, Washington, United States
動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: AI for Researchers and Educators

316A
5 件の発表
2024-05-14 20:00:00
2024-05-14 21:20:00