Application of Artificial Intelligence to Analyze Blood BiochemicalParameters for Remote Patient Monitoring: Opportunities and Limitations

Introduction: The integration of Artificial Intelligence (AI) in analyzing blood biochemical parameters represents a transformative approach to remote patient monitoring, addressing the growing burden of chronic diseases and healthcare resource limitations. This review examines the current state, opportunities, and challenges of AI implementation in interpreting laboratory data for distant patient observation. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science databases covering 2020–2025. The search terms included: artificial intelligence, machine learning, remote patient monitoring, biochemical parameters, and laboratory diagnostics. Results: Modern AI technologies demonstrate high accuracy in predicting pathologies, detecting hidden patterns, and automating result interpretation, with documented success in diagnosing COVID-19, liver diseases, chronic kidney disease, and metabolic syndrome. The technology offers significant advantages, including 24/7 availability of expert interpretation, reduced burden on medical personnel, early detection of condition deterioration, and personalized monitoring through integration of multi-source data. However, substantial barriers persist, including data standardization challenges across different laboratory platforms, limited model validation beyond training populations, unclear regulatory frameworks, and integration difficulties with existing healthcare infrastructure. Critical limitations include preanalytical and analytical errors affecting data quality, lack of unified reference intervals, insufficient evidence base for clinical and economic effectiveness, and resistance from medical professionals. Discussion: AI-driven interpretation of routine biochemical tests appears feasible for remote monitoring and triage, potentially enabling earlier detection of deterioration and reducing clinician workload. Nonetheless, laboratory heterogeneity, preanalytical/analytical variability, and population shifts can undermine generalizability and calibration, underscoring the need for multicenter external validation and a human-in-the-loop implementation with secure data governance. Conclusion: Future directions include developing specialized models for specific pathologies, integrating multi-omic data to generate comprehensive bioprofiles, creating explainable AI systems to increase physician trust, and establishing large-scale clinical trials to validate effectiveness. Success requires interdisciplinary collaboration, with AI positioned as a physician's assistant rather than a replacement.