Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study
The locally deployed LLM can be used for MQA with considerable time-saving and acceptable accuracy in responses. Prompt engineering and fine-tuning may further augment automated data extraction from clinical narratives for the provision of real-time, essential clinical insights.
Authors
- Akhil Vaid (ORCID: https://orcid.org/0000-0002-3343-744X)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- K. Menon (ORCID: https://orcid.org/0000-0003-4589-4661)
- David S. Matteson (ORCID: https://orcid.org/0000-0002-2674-0387)
- Robert Freeman (ORCID: https://orcid.org/0000-0003-4946-6533)
- Michael L. Marin
- Denise Lee (ORCID: https://orcid.org/0009-0000-9203-925X)
Institutions
- Cornell University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-04-07
- DOI
- https://doi.org/10.2196/64544
- Citations
- 7
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 4.58