AI-Based Laboratory Data Interpretation

Abstract

Clinical laboratory data interpretation -- the translation of numerical test results into actionable clinical meaning -- is one of the highest-volume cognitive tasks in medicine, yet it remains prone to systematic errors including failure to recognise clinically significant patterns across multiple analytes, delayed flagging of critical values, and inconsistent integration of laboratory trends with clinical context. Artificial intelligence offers a route to more consistent, context-aware interpretation that processes the full longitudinal laboratory record alongside clinical variables simultaneously. We evaluated five AI laboratory interpretation frameworks across 182,648 patient episodes at 16 European hospitals in Vienna, Tallinn, Rome, Lisbon, and Prague between 2018 and 2023. Frameworks ranged from rule-based critical value alerting through transformer-based longitudinal laboratory pattern recognition with clinical context integration. Performance was benchmarked using our Laboratory Interpretation Accuracy Score (LIAS), integrating diagnostic sensitivity for clinically significant laboratory patterns, false alert rate, time-to-clinician notification, interpretive accuracy versus specialist reference, and clinical outcome impact. The transformer-based contextual framework achieved the highest LIAS (0.926), identifying clinically significant laboratory patterns 6.2 hours earlier than rule-based systems and reducing false critical value alerts by 48.4%. Sepsis-pattern recognition improved from 68.4% sensitivity (rule-based) to 91.2% (transformer), with the largest gains in atypical presentations -- elderly patients with blunted inflammatory responses and immunocompromised patients with masked leucocytosis.