Use of Large Language Models and Artificial Intelligence Tools in Works Submitted to Journal of Clinical Oncology
The Journal of Clinical Oncology shares knowledge and expertise, fosters a robust interchange, and perpetuates valuable learning, all in service of improving outcomes of patients with cancer. To that end, JCO is more than just a collection of articles. The journal provides a forum for the exchange of ideas in which individual voices contribute their unique perspective and experience, providing the human context to the knowledge generated. That conversation between the author and the reader, albeit distant in time and space, is central to our purpose.
Authors
- Angela Cochran (ORCID: https://orcid.org/0000-0002-6255-4389)
- Jonathan W. Friedberg (ORCID: https://orcid.org/0000-0002-1420-5563)
- Emilie Gunn (ORCID: https://orcid.org/0000-0001-6833-0928)
- Stephanie B. Wheeler (ORCID: https://orcid.org/0000-0002-3399-9047)
- Kathy D. Miller (ORCID: https://orcid.org/0000-0002-7125-883X)
- Paul Frankel (ORCID: https://orcid.org/0000-0003-2231-6121)
- Hal Burstein
Institutions
- University of North Carolina at Chapel Hill (US)
Publication Details
- Journal
- UNC Libraries
- Published
- 2025-02-04
- DOI
- https://doi.org/10.17615/gse8-v686
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bristol-Myers Squibb
- Pfizer
- AstraZeneca
- Celgene
- Astex Pharmaceuticals
- Genentech