Enhancing systematic literature reviews with generative artificial intelligence: development, applications, and performance evaluation
The system demonstrates potential to streamline systematic literature reviews, potentially reducing time, cost, and human errors while enhancing evidence generation for HTA submissions.
Authors
- Julie Glasgow
- Majid Rastegar-Mojarad (ORCID: https://orcid.org/0000-0001-6962-3554)
- Surabhi Datta (ORCID: https://orcid.org/0000-0002-5634-2005)
- Conor Liston (ORCID: https://orcid.org/0000-0002-1639-5401)
- Ying Li (ORCID: https://orcid.org/0000-0002-2813-9480)
- Yingxin Xu
- Long He (ORCID: https://orcid.org/0009-0003-6846-2076)
- Kyeryoung Lee
- Hunki Paek (ORCID: https://orcid.org/0009-0000-9916-5654)
- Xiaoyan Wang (ORCID: https://orcid.org/0009-0006-9559-9221)
Institutions
- Regeneron (United States) (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-02-27
- DOI
- https://doi.org/10.1093/jamia/ocaf030
- Citations
- 32
- Primary Topic
- Meta-analysis and systematic reviews
- Type
- article
- Field-Weighted Citation Impact
- 44.75