Accuracy and consistency of publicly available Large Language Models as clinical decision support tools for the management of colon cancer
Publicly available LLM applications often provide verbose responses with vague or inaccurate information regarding colon cancer management. Significant optimization is required before use in formal CDS.
Authors
- Alexa J. Hughes (ORCID: https://orcid.org/0000-0002-2972-1253)
- Rachel E. Patzer (ORCID: https://orcid.org/0000-0003-2573-9963)
- Karl Y. Bilimoria (ORCID: https://orcid.org/0000-0001-5067-1345)
- Anita Turk (ORCID: https://orcid.org/0000-0002-4159-5376)
- Sanjay Mohanty (ORCID: https://orcid.org/0000-0003-0295-3457)
- Andrew A. Gonzalez (ORCID: https://orcid.org/0000-0002-7406-2314)
- Anthony D. Yang (ORCID: https://orcid.org/0000-0002-9053-0974)
- Kristen N. Kaiser
- Ryan J. Ellis
Institutions
- Regenstrief Institute (US)
- Indiana University School of Medicine
- Indiana University – Purdue University Indianapolis (US)
Publication Details
- Journal
- Journal of Surgical Oncology
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1002/jso.27821
- Citations
- 16
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.47