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

Laboratory medicine plays a crucial role in clinical decision-making, yet result in-terpretation often remains challenging for patients. This study evaluates the effec-tiveness of AI-powered conversational systems in interpreting laboratory test results, utilizing a closed-box training approach for Claude-based virtual conversational chatbot, focusing exclusively on laboratory data interpretation without clinical di-agnosis. The system was tested using 30 laboratory reports from two different Italian laboratories, encompassing various biochemical parameters and measurement standards. The laboratories utilized different analytical platforms and methodologies, allowing us to evaluate the chatbot's ability to interpret results across diverse in-strumental settings. The interpretation accuracy of Claude AI chatbot was rigorously evaluated through a peer review process involving three independent medical re-viewers with extensive experience in laboratory medicine. Significantly, the Claude model showed zero hallucinations. The excellent performance was attributed to the closed-box training environment, high-quality domain-specific prompts, and pure generation mechanisms without external data access. This study suggests 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 important implications for patient empowerment and healthcare communication efficiency.