用AI整合最新医学文献，生成可追溯的可信医疗问答。
MedSEBA: Synthesizing Evidence-Based Answers Grounded in Evolving Medical Literature
- 基于大模型与PubMed动态检索，生成有依据的医学回答。
- 展示研究共识随时间演变，支持/反对证据一目了然。
- 适合医生和普通用户，兼顾专业与易懂。
在数字时代，人们常通过网络获取医疗建议，但在线内容泛滥导致真假难辨。每年数百万篇医学研究发表，研究人员难以追踪最新成果。这些研究结论可能相互矛盾，传统搜索工具无法反映这种动态变化。为此，我们提出MedSEBA，一个交互式AI系统，用于合成基于证据的医学问题回答。该系统利用大语言模型生成连贯、清晰的答案，并将其基于从PubMed数据库动态检索的可信研究。答案包含可追溯至具体研究的关键点与论证。平台还提供核心研究对特定医学主张的支持或反驳程度概览，并可视化研究共识随时间的变化。用户研究表明，医学专家与普通用户均认为系统可用、有用，回答可信且信息丰富。因此，该系统适用于日常健康咨询与高级科研洞察。
原文摘要 · Abstract (English)
In the digital age, people often turn to the Internet in search of medical advice and recommendations. With the increasing volume of online content, it has become difficult to distinguish reliable sources from misleading information. Similarly, millions of medical studies are published every year, making it challenging for researchers to keep track of the latest scientific findings. These evolving studies can reach differing conclusions, which is not reflected in traditional search tools. To address these challenges, we introduce MedSEBA, an interactive AI-powered system for synthesizing evidence-based answers to medical questions. It utilizes the power of Large Language Models to generate coherent and expressive answers, but grounds them in trustworthy medical studies dynamically retrieved from the research database PubMed. The answers consist of key points and arguments, which can be traced back to respective studies. Notably, the platform also provides an overview of the extent to which the most relevant studies support or refute the given medical claim, and a visualization of how the research consensus evolved through time. Our user study revealed that medical experts and lay users find the system usable and helpful, and the provided answers trustworthy and informative. This makes the system well-suited for both everyday health questions and advanced research insights.
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