Leveraging artificial intelligence for early detection and prediction of acute kidney injury in clinical practice
This makes it a valuable tool for enabling proactive clinical interventions. The proposed model contributes to the expanding landscape of AI-enabled healthcare solutions for AKI, supporting the broader initiative to incorporate intelligent systems into clinical workflows to improve patient care and outcomes.
Authors
- Ming Lei (ORCID: https://orcid.org/0000-0002-1153-4791)
- Bo Liang (ORCID: https://orcid.org/0009-0002-6547-8577)
- Cui Ma (ORCID: https://orcid.org/0000-0001-5603-5964)
Institutions
- Shanghai University (CN)
- Shanghai University of Medicine and Health Sciences (CN)
- Xinxiang Central Hospital (CN)
- Henan Medical University (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-09-24
- DOI
- https://doi.org/10.3389/fphys.2025.1612900
- Citations
- 1
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 1.43