Augmenting Large Language Models With Automated, Bibliometrics-Powered Literature Search for Knowledge Distillation: A Pilot Study for Common Spinal Pathologies
We demonstrate the rapid, automated summarization of seminal articles for four common spinal pathologies, with a generalizable workflow implemented using consumer-grade hardware. Our tandem strategy fuses bibliometrics and artificial intelligence to bridge the gap toward fully automated knowledge distillation, obviating the need for manual literature review and article selection.
Authors
- Anthony Frempong‐Boadu (ORCID: https://orcid.org/0000-0001-5958-0757)
- Douglas Kondziolka (ORCID: https://orcid.org/0000-0001-8925-7344)
- Daniel Alexander Alber (ORCID: https://orcid.org/0000-0001-7957-5170)
- Ilya Laufer (ORCID: https://orcid.org/0000-0003-0092-5030)
- Darryl Lau (ORCID: https://orcid.org/0000-0003-4146-7590)
- Adhith Palla (ORCID: https://orcid.org/0000-0002-0216-7790)
- Eric K. Oermann (ORCID: https://orcid.org/0000-0002-1876-5963)
- David B. Kurland (ORCID: https://orcid.org/0000-0002-4074-7497)
- Daniel N. de Souza (ORCID: https://orcid.org/0009-0008-2752-6965)
Institutions
- Neurosciences Institute (US)
- NYU Langone Health (US)
- New York University (US)
Publication Details
- Journal
- Neurosurgery
- Published
- 2025-01-16
- DOI
- https://doi.org/10.1227/neu.0000000000003354
- Citations
- 5
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 4.03