Comparative Analysis of Generative Pre-Trained Transformer Models in Oncogene-Driven Non–Small Cell Lung Cancer: Introducing the Generative Artificial Intelligence Performance Score
This study highlights the rapid improvement of generative AI in matching treatment recommendations with biomarkers in precision oncology. Although the rate of hallucinations improved in the GPT-4 model, future generative AI use in clinical care requires high levels of accuracy with minimal to no room for hallucinations. The GP-S represents a novel metric quantifying generative AI utility in health care compared with national guidelines, with potential adaptation beyond precision oncology.
Authors
- Ryan Huu-Tuan Nguyen (ORCID: https://orcid.org/0000-0002-8863-6649)
- Noor Naffakh (ORCID: https://orcid.org/0000-0001-5195-8313)
- Zacharie Hamilton (ORCID: https://orcid.org/0009-0009-0904-7220)
- Shikha Jain (ORCID: https://orcid.org/0000-0002-5684-7898)
- Zhengjia Chen (ORCID: https://orcid.org/0000-0002-6301-4931)
- Natalie Reizine (ORCID: https://orcid.org/0000-0001-9240-9943)
- Larry G. Kessler (ORCID: https://orcid.org/0000-0002-8024-5424)
- Vijayakrishna K. Gadi (ORCID: https://orcid.org/0000-0002-5427-7465)
- Frank Weinberg (ORCID: https://orcid.org/0000-0002-0094-3725)
- Christopher Bun
- Aseem Aseem
Institutions
- University of Washington (US)
- University of Illinois Chicago (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1200/cci.24.00123
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81