## 3 Results  
A total of 1824 items were analyzed (19 items for each of the 96 students). The grading times are presented in Table 2. Notably, the AI was significantly faster, completing the analysis in just 24 min, which represents less than one-fifth of the time required by an expert evaluator. The average score assigned by the AI was 8.88, with a standard deviation of 2.96. On average, human evaluators are assigned higher scores than AI. The combined average \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grader** & **Subject** & **Total Items** &
\begin{tabular}{c} **Correction** \\ **Time** \\ \end{tabular} & **Breaks** & **Total Time** \\ \hline Expert 2 & Cardiology & 1824 & 2 h 15 min & 10 min & 2 h 25 min \\ Inexperienced & Cardiology & 1824 & 4 h & 10 min & 4 h 10 min \\ AI & Cardiology & 1824 & 2 4 min & N/A & 24 min \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total items analyzed and correction times for the AI, an expert, and an inexperienced grader. of both experts was 12.39, with a standard deviation of 3.22. The inexperienced grader also had an average score that was higher than the AI's but lower than that of the expert evaluators (Table 3). The ICC was calculated (Table 4) to analyze the correlation between the scores assigned by the different graders. In comparing single measures between the experts and the AI (EXP1-EXP2-AI), an ICC of 0.77 (95% CI: 0.699-0.834) was obtained. The correlation was even higher for average measures, reaching an ICC of 0.91 (95% CI: 0.875-0.938). Notably, the AI showed a higher performance compared to the inexperienced grader, whose correlation with the AI was lower, with an ICC of 0.72 (95% CI: 0.608-0.804) for single measures. A Greenhouse-Geisser correction was applied to analyze the differences in the mean scores assigned, and a repeated measures analysis of variance (ANOVA) was performed, as the assumption of sphericity was not met according to [PERSON]'s test. The analysis showed (Table 5) statistically significant differences between graders (F = 118.17, \(p\) < 0.001), indicating that the AI and human evaluators' scores were inconsistent. Specific contrasts between graders revealed that the AI evaluated the reports with significant differences compared to Expert 1 (F = 206.246, \(p\) < 0.001) and Expert 2 (F = 211.246, \(p\) < 0.001) and the combined mean of both experts (F = 235.862, \(p\) < 0.001). Similarly, significant differences were found between the inexperienced grader and the AI, although these differences were minor compared to the experts (F = 86.486, \(p\) < 0.001). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Single Measures**} & **Average Measures** \\ \hline \multirow{3}{*}{EXP1-EXP2} & \multirow{3}{*}{IC (95)} & 0.88 & 0.94 \\  & & (0.826–0.919) & (0.905–0.958) \\  & \(p\) & \textless{}0.001 & \textless{}0.001 \\ \hline \multirow{3}{*}{EXP1-EXP2-INEXP-AI} & \multirow{3}{*}{IC (95)} & 0.79 & 0.97 \\  & & (0.725–0.842) & (0.913–0.955) \\  & \(p\) & \textless{}0.001 & \textless{}0.001 \\ \hline \multirow{3}{*}{EXP1-EXP2-AI} & \multirow{3}{*}{IC (95)} & 0.77 & 0.91 \\  & \(p\) & \textless{}0.001 & \textless{}0.001 \\ \hline \multirow{3}{*}{INEXP-AI} & \multirow{3}{*}{IC (95)} & 0.72 & 0.84 \\  & \(p\) & \textless{}0.001 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Intraclass correlation coefficient displaying the correlation between AI, experts (EXP1, EXP2), and the inexperienced grader (INEXP). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Maximum** & **Minimum** & **Mean** & **Standard Deviation** \\ \hline AI & 16.5 & 3 & 8.88 & 2.96 \\ EXP1 & 18 & 4.5 & 12.45 & 3.42 \\ EXP2 & 18 & 5 & 12.33 & 3.22 \\ INEXP & 17 & 3 & 11.05 & 3.16 \\ EXPERTS’ & & & 12.39 & 3.22 \\ MEAN & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of means and standard deviations for the AI, experts, and the inexperienced grader.