Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis
LLMs hold transformative potential for medical diagnostics but require rigorous validation, bias mitigation, and multimodal integration to address real-world complexities. Future research should prioritize explainable artificial intelligence frameworks, specialty-specific optimization, and international regulatory harmonization to ensure equitable and safe clinical deployment.
Authors
- Bin Xu (ORCID: https://orcid.org/0000-0001-6338-4230)
- Aozhe Zhang
- Fen Xiao (ORCID: https://orcid.org/0000-0001-7375-3482)
- Zhiying Duan
- Ruiting Li
- Yuemeng Yang (ORCID: https://orcid.org/0009-0008-9035-2689)
- Yanping Li
- Qiong Zhang (ORCID: https://orcid.org/0000-0002-5039-4648)
- Hui Li (ORCID: https://orcid.org/0000-0002-7516-7126)
- Yuanyuan Sun
- Tianli Yang
- Jing Zhao (ORCID: https://orcid.org/0000-0002-3658-3274)
- Jingjing Chen (ORCID: https://orcid.org/0000-0002-1857-8498)
- Qin Hu
- Hankun Su (ORCID: https://orcid.org/0009-0008-1748-1978)
Institutions
- Central South University (CN)
- Second Xiangya Hospital of Central South University (CN)
- Xiangya Hospital Central South University (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-06-09
- DOI
- https://doi.org/10.2196/72062
- Citations
- 20
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 13.09