Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology
ML models have the potential to transform LN management by enabling earlier flare detection, personalized treatment strategies, and non-invasive disease monitoring. To achieve clinical integration, future research must prioritize robust validation, interoperability with electronic health records, and transparent model interpretability. Bridging the gap between computational performance and real-world application could substantially improve outcomes and quality of life for LN patients.
Authors
- Henry Joseth González-Torres (ORCID: https://orcid.org/0000-0001-7434-4568)
- Roberto Navarro Quiroz (ORCID: https://orcid.org/0000-0002-3604-3702)
- Gustavo Aroca (ORCID: https://orcid.org/0000-0002-9222-3257)
- Alex Domínguez-Vargas (ORCID: https://orcid.org/0000-0002-3984-8653)
- Carlos Guido Musso
- Diego Fernando Garcia-Bañol
- Adrianny Mahelis Arias-Choles
- Silvia Aldana-Peréz
Institutions
- Hospital Italiano de Buenos Aires (AR)
- University of the Coast (CO)
- Universidad Simón Bolívar (CO)
- Data Management (Italy) (IT)
- Universidade Estadual Paulista (Unesp) (BR)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-10-29
- DOI
- https://doi.org/10.3389/fmed.2025.1686057
- Citations
- 2
- Primary Topic
- Systemic Lupus Erythematosus Research
- Type
- article
- Field-Weighted Citation Impact
- 3.34