文学:生物医学文献审查和引文检索代理
Alon Gorenshtein1, Kamel Shihada2, Moran Sorka3
1Department of Neurology, Rambam Health Care Campus, Haifa, Israel; Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel; AI in Neurology Laboratory, Ruth and Bruce Rapaport Faculty of Medicine, Technion Institute of Technology, Haifa, 3525408, Israel.
Computers in biology and medicine
|May 18, 2025
概括
一个新的系统,LITERAS,在人工智能辅助的文献评论中显著提高了引用准确性. 该工具通过仅使用同行评审的来源来提高可靠性,优于其他大型语言模型 (LLM).
科学领域:
- 生物医学信息学 生物医学信息学
- 研究中的人工智能.
背景情况:
- 大型语言模型 (LLM) 通常会产生不准确或伪造的引用.
- 这导致人工智能驱动的参考检索系统的准确性降低.
- 在科学研究中,需要非常精确和可靠的引用工具.
研究的目的:
- 开发一个开源的,多AI代理系统 (LITERAS) 准确的文献审查.
- 为了提高AI产生的科学内容的引用精度和可靠性.
- 评估LITERAS与五个医学学科的最先进的LLM相比.
主要方法:
- 开发了LITERAS,通过PubMed API和代理间通信集成MEDLINE搜索.
- 将LITERAS与Sonar和Sonar-Pro在瘤学,心脏病学,风湿病学,精神病学和传染病领域进行了比较.
- 评估引用的可信性,准确性,确认性和来源质量.
主要成果:
- 利特拉斯实现了99.82%的引用精度,与索纳 (100%) 和索纳-Pro (99.93%) 相比.
- 在引用准确性方面,LITERAS显著优于Sonar (96.81%与89.07%相比),并且与Sonar-Pro (96.33%) 相匹配.
- 利特拉斯专门使用Q1-Q2同行评审期刊 (0%非学术期刊),不同于Sonar (35.60%) 和Sonar-Pro (6.47%).
结论:
- 利特拉斯展示了对最近学术文章的卓越检索能力,并且比现有的LLM方法生成更全面的报告.
- 该系统的多代理设计尽量减少幻觉,并优先考虑最近的出版物.
- 这项研究为提高人工智能辅助文献审查工具的可靠性提供了有价值的见解.
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