Feasibility of federated learning for the development of AI solutions in laboratory diagnostics from a privacy perspective: a systematic review
DOI:
https://doi.org/10.5753/isys.2026.7589Keywords:
Federated Learning, Laboratory Diagnosis, Data Privacy, Artificial Intelligence in Healthcare, LGPD (Brazilian General Data Protection Law), Systematic ReviewAbstract
The development of artificial intelligence solutions in laboratory diagnostics faces a structural dilemma: the requirement for large data volumes for training conflicts with strict health data protection regulations (GDPR, LGPD, HIPAA). This systematic review investigated federated learning as a methodological alternative that enables AI model training without centralizing sensitive data. Analyzing 23 articles (2021-2025), we found that 56.5% of studies demonstrated technical feasibility, although only 26.1% explicitly addressed regulatory compliance. Results reveal a technically promising field, but with a critical gap between technical maturity and regulatory adequacy.
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