Evaluating the efficacy of large language models in cardio-oncology patient education: a comparative analysis of accuracy, readability, and prompt engineering strategies
Publicly available LLMs provide largely accurate responses to cardio-oncology questions, yet their utility is constrained by inconsistent comprehensiveness and sensitivity to prompt design. While simplifying language improves readability, it risks compromising clinical relevance. Tailored fine-tuning and specialized evaluation frameworks are essential to optimize LLMs for patient education in cardio-oncology.
Authors
- Yuhui Huang (ORCID: https://orcid.org/0000-0002-2392-0630)
- Weiran Xu
- Haojie Zhu
- Hao Xu
- Zhao Wang
- Lin Liang
- Chen He
Institutions
- Shanghai Jiao Tong University (CN)
- Capital Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- Shanghai First People's Hospital (CN)
- Peking University Third Hospital (CN)
- Fu Wai Hospital (CN)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-01-13
- DOI
- https://doi.org/10.3389/frai.2025.1693446
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundamental Research Funds for the Central Universities