一种优化的无代码人工智能方法,用于有效和准确的骨类有机体研究文献选
Jiaxu Zheng1, Janak Lal Pathak1, Shangyan Li1
1Department of Basic Oral Medicine, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou 510182, China.
Journal of biomedical informatics
|September 11, 2025
概括
这项研究优化了AI文献选工具LitSuggest,显著提高了生物医学研究的效率和准确性. 该系统将手工工作量减少90%以上,有助于在有机体再生等领域的知识发现.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 再生医学是一种再生医学.
背景情况:
- 生物医学文献的指数增长需要先进的工具来有效地发现知识.
- 传统的文学选方法正在变得不可持续.
- 临床医生和研究人员往往不熟悉新兴的人工智能 (AI) 工具用于文献检索.
研究的目的:
- 为了优化LitSuggest,一个用户友好的,无代码的基于AI的文献选系统.
- 为人工智能驱动的文献分析建立标准化的操作工作流程.
- 评估系统在基于器官的骨组织工程领域的性能.
主要方法:
- 优化 LitSuggest AI 系统用于文学选.
- 为系统运行制定标准化工作流程.
- 在基于器官的骨组织工程研究中的案例研究应用.
- 整合相关性评分用于高级文献分析.
主要成果:
- 优化的LitSuggest系统实现了高准确度 (98.83%),精度 (76.19%),回忆 (83.33%) 和F1得分 (79.60%).
- 手动文献选工作量减少了90%以上.
- 相关性评分确定了主要的研究贡献者 (中国,美国),并突出了复杂/遗传疾病有机体模型的潜力.
结论:
- 像LitSuggest这样的AI驱动的文献选系统通过专注于高价值出版物来提高研究效率.
- 优化的系统为未来的骨器官再生研究方向提供了宝贵的见解.
- 这种人工智能方法在生物医学领域具有广泛的适用性,用于改进知识发现.
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