AutoReporter: development of an artificial intelligence tool for automated assessment of research reporting guideline adherence
Large language models can feasibly automate reporting guideline adherence assessments for scalable quality control in scientific research reporting. AutoReporter is publicly accessible at https://autoreporter.streamlit.app.
Authors
- M. Lee
- Umair Tahir (ORCID: https://orcid.org/0009-0005-1913-7335)
- Srinivas Raman (ORCID: https://orcid.org/0000-0001-5688-9628)
- Ealia Khoshkish
- Henry C Y Wong
- Seungmin Lee
- David Chen
- Patrick Li
- Tony Ning
Institutions
- BC Cancer Agency (CA)
- University of British Columbia (CA)
- University of Toronto (CA)
- Princess Margaret Cancer Centre (CA)
- National University Cancer Institute, Singapore (SG)
- Princess Margaret Hospital (HK)
- McMaster University (CA)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-12-09
- DOI
- https://doi.org/10.1093/jamia/ocaf223
- Primary Topic
- Meta-analysis and systematic reviews
- Type
- article
- Field-Weighted Citation Impact
- 0.00