Development and Evaluation of Artificial Intelligence-Based Two-Step Model for Automated Serum Quality Assessment in Clinical Laboratories
The AI-powered serum quality software demonstrated high accuracy in preanalytical serum quality assessment, offering a performance that meets or exceeds that of both automated and manual measurements. This tool can enhance laboratory efficiency and reduce the error risks associated with subjective visual evaluations.
Authors
- Yuxuan Xiong (ORCID: https://orcid.org/0000-0002-7588-028X)
- Fu Yang (ORCID: https://orcid.org/0000-0002-3151-1765)
- Weixin Chen (ORCID: https://orcid.org/0000-0001-7633-5489)
- Zikun Ma (ORCID: https://orcid.org/0000-0001-5361-0521)
- Ran Han
- Ye Cui (ORCID: https://orcid.org/0000-0002-2906-3289)
- Qinglin Liu
- Chenxi Zhang
- Zhifang Sui
Institutions
- Beijing University of Chinese Medicine (CN)
- Beijing Normal University (CN)
- Roche (China) (CN)
Publication Details
- Journal
- Annals of Laboratory Medicine
- Published
- 2026-03-09
- DOI
- https://doi.org/10.3343/alm.2025.0509
- Primary Topic
- Bacterial Identification and Susceptibility Testing
- Type
- article
- Field-Weighted Citation Impact
- 0.00