AI integration in nephrology: evaluating ChatGPT for accurate ICD-10 documentation and coding
ChatGPT 4.0 can significantly improve ICD-10 coding accuracy in nephrology through case scenarios for pre-visit testing, potentially reducing healthcare professionals' workload. However, the small error percentage underscores the need for ongoing review and improvement of AI systems to ensure accurate reimbursement, optimal patient care, and reliable research data.
Authors
- Wisit Cheungpasitporn (ORCID: https://orcid.org/0000-0001-9954-9711)
- Iasmina Craici
- Jing Miao (ORCID: https://orcid.org/0000-0003-0642-9740)
- Justin H. Pham (ORCID: https://orcid.org/0009-0009-5913-7363)
- Supawadee Suppadungsuk (ORCID: https://orcid.org/0000-0003-1597-2411)
- Charat Thongprayoon (ORCID: https://orcid.org/0000-0002-8313-3604)
- Yasir Abdelgadir
- Michael A. Mao (ORCID: https://orcid.org/0000-0003-1814-7003)
Institutions
- Mayo Clinic (US)
- Mahidol University (TH)
- Mayo Clinic in Arizona (US)
- Ramathibodi Hospital (TH)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2024-09-02
- DOI
- https://doi.org/10.3389/frai.2024.1457586
- Citations
- 17
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 2.04