The clinical application potential assessment of the Deepseek-R1 large language model in lung cancer
Deepseek-R1 significantly outperformed junior oncologists in terms of diagnostic accuracy and treatment decision-making, particularly in complex and dynamic clinical situations. While limitations remain in its ethical reasoning, the model holds substantial potential for supporting junior physicians, contributing to multidisciplinary discussions, and optimizing treatment pathways.
Authors
- Zhaorong Yang (ORCID: https://orcid.org/0000-0003-4414-1545)
- Shihao Zhou (ORCID: https://orcid.org/0000-0002-5644-6998)
- Xiuli Yang (ORCID: https://orcid.org/0000-0001-8277-4285)
- Ming Guan (ORCID: https://orcid.org/0000-0002-8796-2653)
- Baoyan Ji
- Xiaowan Xu
- Dan Fan (ORCID: https://orcid.org/0000-0003-1197-1737)
- Hongli Chen (ORCID: https://orcid.org/0009-0001-6466-1823)
- Zhibo Liu
Institutions
- Qinghai University (CN)
- Qinghai Red Cross Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-02
- DOI
- https://doi.org/10.3389/fonc.2025.1601529
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65