Use of Artificial Intelligence Chatbots in Interpretation of Clinical Chemistry and Laboratory Medicine Reports: A Standardized Approach

Abstract

Laboratory medicine is crucial for clinical decision-making, yet result interpretation often remains challenging for patients. This study evaluates the effectiveness of an Artificial Intelligence (AI)-powered conversational system in interpreting laboratory test results, utilizing a closed-box training approach for a Claude-based virtual chatbot focused exclusively on laboratory data interpretation without clinical diagnosis. The system was tested using 100 laboratory reports from three Italian laboratories, encompassing diverse biochemical parameters and measurement standards. The laboratories employed different analytical platforms and methodologies, enabling evaluation of the chatbot’s ability to interpret results across varied instrumental settings. The interpretation accuracy was rigorously assessed through peer review by three independent medical experts with extensive laboratory medicine experience. The Claude model demonstrated complete accuracy with zero hallucinations, attributed to the controlled training environment, domain-specific prompts, and pure generation mechanisms without external data access. Patient feedback from 70 participants showed high satisfaction rates, with 90% providing positive ratings. This study demonstrates that carefully designed AI models can effectively bridge the gap between raw laboratory data and patient understanding, potentially transforming laboratory reporting systems while maintaining high accuracy and avoiding diagnostic territory. These findings have significant implications for patient empowerment and healthcare communication efficiency.

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