## 4 Discussion  
Our study demonstrates the remarkable potential of AI systems, particularly the Claude-based conversational chatbot, in transforming how laboratory results are interpreted and communicated. A key strength of our findings lies in the chatbot's demonstrated ability to accurately interpret laboratory results across different analytical platforms and units of measurement. The heterogeneity of analytical platforms used in this study--including Roche cobas 8000, Abbott Architect ci16200, DiaSorin LIAISON XL, and Sebia CAPILLARYS 3--presented a significant challenge that the AI system successfully overcame. For instance, the chatbot correctly interpreted hemoglobin values reported in both g/dL and mmol/L, and creatinine results in both mg/dL and mmol/L, maintaining consistency in its interpretations regardless of the unit system used. This capability is particularly significant given that laboratories often use different reference ranges and reporting units, which can lead to confusion among healthcare providers and patients. Our findings align with recent studies by [PERSON] [8] who demonstrated the potential of large language models in enhancing laboratory result communication, particularly in pathology reports. However, our work differs significantly in its focused approach to non-diagnostic interpretation and zero-hallucination rate. Similarly to the work of [PERSON] et al. [12] in patient communication, we prioritized clarity and accessibility while maintaining technical accuracy. The zero-hallucination rate achieved in our study contrasts with the findings of [PERSON] et al. [20], who reported a 12-18% error rate when using general-purpose AI models for laboratory interpretation without domain-specific training. Figure 1: Distribution of user satisfaction levels. Prior to selecting Claude for our study, we conducted a comparative evaluation of three leading AI models, Claude 3 Opus, GPT-4, and Gemini 1.5 Pro, using a test set of 25 laboratory reports covering diverse parameters and abnormality patterns. Claude 3 Opus demonstrated superior performance with 100% interpretation accuracy compared to GPT-4 (93.2%) and Gemini 1.5 Pro (91.5%). The hallucination rates were 0% for Claude, 4.3% for GPT-4, and 5.1% for Gemini. Claude also scored highest on context preservation (4.7/5) and explanation clarity (4.6/5). These results, along with Claude's robust privacy protection features and ability to handle complex prompts, informed our decision to proceed with Claude for the full study. The controlled training environment of our AI model proved crucial in achieving consistent and reliable interpretations, completely eliminating the risk of hallucinations, while maintaining strict adherence to privacy and security requirements [19]. This approach is particularly relevant given our finding that while 96% of Italian laboratories use interpretative comments, most lack standardized procedures within their teams. The implementation of high-quality, domain-specific prompts enabled the model to provide accurate interpretations of laboratory parameters without venturing into broader clinical assessments, effectively addressing the current inconsistencies in laboratory reporting practices. To rigorously test the chatbot's capabilities and avoid selection bias, we deliberately included challenging edge cases and complex profiles in our evaluation set. These included critical abnormal results (5% of reports), complex multi-parameter abnormalities (28% of reports), contradictory findings, and age-specific challenges (12% of reports from pediatric or geriatric populations). The chatbot's performance was consistently strong across all these categories, with only minor variations in explanation quality. Unlike the approach taken by [PERSON] et al. [16], who evaluated AI systems across a broader spectrum of medical information, our focused scope on laboratory data interpretation allowed for more precise training and validation, potentially explaining our superior accuracy rates. This aligns with [PERSON] et al.