## 3 Results
### Patient Feedback  
A survey was conducted using a mixed-methods approach combining quantitative Likert scale responses with qualitative feedback. Data collection occurred in December 2024, involving 70 subjects from diverse demographic backgrounds (Table 2). The survey was conducted to evaluate the following. Understanding Results:  
\"The way complex medical terms were broken down made everything crystal clear. It's like having a medical translator.\"  
\"I particularly appreciated how the AI explained the relationship between different test results. It helped me see the bigger picture.\"  
\"The explanations were detailed enough to be informative but simple enough that I didn't need a medical degree to understand them.\"  
Anxiety Reduction:  
\"Understanding why my cholesterol was slightly elevated and what it meant in context helped me feel less worried.\"  
\"The explanations helped me understand that not every 'abnormal' result is cause for panic.\"  
\"Having clear explanations available immediately after seeing my results prevented me from spiraling into worst-case scenarios.\"  
Doctor Consultations:  
\"I felt more confident discussing my results with my doctor because I already had a basic understanding of what they meant.\"  
\"The explanations helped me formulate specific questions for my doctor instead of just general concerns.\"  
\"My consultation was more productive because I could focus on treatment options rather than just trying to understand the basics.\"  
A survey was conducted involving 70 subjects to evaluate the understanding results, anxiety reduction, and doctor consultations. Interviews were conducted within 48 h of persons receiving their AI-interpreted results to ensure accurate recall and relevant feedback. The analysis of score distribution demonstrates strong overall satisfaction: 42% of participants gave perfect scores (5/5), while 90% provided positive ratings (4/5 or higher). Only a small percentage gave neutral (8%) or negative (2%) responses, confirming the AI interpretation system's effectiveness in meeting user needs. Table 3 shows the Likert scale responses from 70 participants rating various aspects of the AI system on a scale of 1-5. The results indicate strong positive feedback across all \begin{table}
\begin{tabular}{l c} \hline \hline
**Evaluation Criteria** & **Mean Score (1–5)** \\ \hline Age range & 18–75 years \\ Gender & 52\% female \\ Education levels: & \\ High school & 40\% \\ Bachelor’s degree & 35\% \\ Graduate degree & 15\% \\ Other & 10\% \\ Prior experience with lab results: & \\ Frequent & 30\% \\ Occasional & 45\% \\ Rare & 25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Demographics and diversity. evaluation criteria. Technical Terms Explanation received the highest mean score of 4.5, followed by Ease of Understanding Results at 4.4. Overall Clarity of AI Interpretations scored 4.3, while Usefulness of Reference Range Context and Helpfulness for Doctor Discussions scored 4.2 and 4.1, respectively. These consistently high scores above 4.0 suggest that users found the AI system effective across all measured dimensions. Table 4 presents a qualitative analysis of patient feedback, organized into three main themes. Improved Understanding was the most prevalent theme, with 92% of participants providing positive comments about how the AI helped them comprehend their medical results. Reduced Anxiety emerged as another significant benefit, with 78% of participants noting how the explanations helped alleviate their concerns about test results. Better Physician Consultations was reported by 85% of participants, who felt better prepared for their medical appointments and more confident in discussing their results with healthcare providers. Figure 1 complements these findings by showing the distribution of responses across the same evaluation criteria from Table 3. The stacked bar chart reveals that the majority of responses fall into the \"Extremely Satisfied\" and \"Satisfied\" categories for all criteria.