Evaluation of error detection and treatment recommendations in nucleic acid test reports using ChatGPT models
The detection capability of some of medical laboratory report errors and the accuracy and reliability of treatment recommendations of GPT models was competent, especially, potentially reducing work hours and enhancing clinical decision-making.
Authors
- Wenjie Zhou (ORCID: https://orcid.org/0000-0002-7626-4032)
- Kai Jin (ORCID: https://orcid.org/0000-0003-4369-2417)
- Jianghua Yang (ORCID: https://orcid.org/0000-0002-8054-2349)
- Wenzheng Han (ORCID: https://orcid.org/0000-0003-0998-6729)
- Gang Feng (ORCID: https://orcid.org/0000-0002-8378-8248)
- Guang Chen (ORCID: https://orcid.org/0000-0003-1778-8730)
- Qing Chen (ORCID: https://orcid.org/0000-0002-1276-1064)
- LI Xiao-ning (ORCID: https://orcid.org/0009-0004-5621-4769)
- Xudong Xu (ORCID: https://orcid.org/0000-0002-7271-1835)
- Chao Wan (ORCID: https://orcid.org/0000-0001-8643-6662)
- Rui Shan (ORCID: https://orcid.org/0000-0002-6181-0061)
- Yuxuan Yang (ORCID: https://orcid.org/0009-0000-5292-713X)
Institutions
- Wannan Medical College (CN)
- Shanghai Clinical Research Center (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
Publication Details
- Journal
- Clinical Chemistry and Laboratory Medicine (CCLM)
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1515/cclm-2025-0089
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.96
Funders
- National Natural Science Foundation of China