用AI工具辅助评估临床试验论文的科研诚信，提升评审一致性与可追溯性。
Authoring and Management of Transparent Research Integrity Assessments of Randomised Clinical Trial Publications Using LLM-assisted Tools and Provenance Knowledge Graphs
- 基于LLM构建交互式工具，按标准框架辅助人工评估科研诚信。
- 建立140个临床试验的诚信评估记录，覆盖95篇发表论文。
- 通过知识图谱实现评估过程可追溯，适合医学评审与指南制定者使用。
随机对照试验（RCT）的系统评价常被用于制定临床诊疗指南，其证据质量需符合高科研诚信标准，以避免低质或虚假研究影响临床决策。然而，评估已发表RCT的科研诚信是一项复杂任务，依赖人工且易产生主观差异。本文介绍INSPECT-AI，一个基于大模型的交互式工具，依据社区认可的INSPECT-SR框架，结合科研诚信溯源与证据本体（RIPE-O），协助人类评审员开展科研诚信评估。此外，我们构建了科研诚信溯源与证据知识图谱（RIPE-KG），包含由INSPECT-AI生成的140项专家级科研诚信评估，涵盖95篇已发表的RCT论文。
原文摘要 · Abstract (English)
Systematic reviews of Randomised Controlled Trials (RCTs) are routinely used as evidence for clinical care guidelines. Such evidence has to meet high research integrity standards to prevent low quality or false research outputs influencing the clinical care. However, assessing research integrity of published RCTs is a complex process requiring manual effort, and potentially resulting in diverse opinions of the human assessors. This paper describes INSPECT-AI, an LLM-based interactive tool that assists human reviewers with research integrity assessments of published RCTs based on the community approved INSPECT-SR framework, and the Research Integrity Provenance and Evidence ontology (RIPE-O) for documenting the provenance of the assessment process. In addition, we present the Research Integrity Provenance and Evidence knowledge graph (RIPE-KG), an initial set of 140 expert research integrity assessments of 95 RCT publications generated by INSPECT-AI and described using RIPE-O.
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