## 4 Discussion  
The AI-based CAD system is drawing significant attention due to its potential to revolutionize the diagnostic workflow, improve diagnostic accuracy among observers, reduce costs, and decrease the workload of healthcare personnel. The role of AI is not merely to replicate human cognitive processes but to enhance and augment them, facilitating a higher level of diagnostic precision that could lead to more effective treatment plans. This shift toward AI-centered diagnostic methods could also redefine the role of healthcare  
Figure 4: Area under the receiver operating characteristic curve (AUROC) for each dataset: dataset A (orange), dataset B (purple), dataset C (light blue), dataset D (blue) and dataset E (yellow). (**a**) ADCmap for segmentation volume; (**b**) T2 WI for segmentation volume; (**c**) ADCmap for normal volume; (**d**) T2 WI for normal volume. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} \\ \cline{3-6}  & & \(\mathbf{GS\geq 7}\) & \(\mathbf{GS<7}\) & \(\mathbf{GS\geq 7}\) & \(\mathbf{GS<7}\) \\ \hline \multirow{2}{*}{Segmentation volume} & ADCmap & 0.73 \(\pm\) 0.13 & 0.60 \(\pm\) 0.11 & 0.50 \(\pm\) 0.04 & 0.82 \(\pm\) 0.06 \\  & T2 WI & 0.57 \(\pm\) 0.13 & 0.72 \(\pm\) 0.13 & 0.53 \(\pm\) 0.09 & 0.76 \(\pm\) 0.04 \\ \hline \multirow{2}{*}{Normal volume} & ADCmap & 0.34 \(\pm\) 0.06 & 0.88 \(\pm\) 0.04 & 0.61 \(\pm\) 0.07 & 0.72 \(\pm\) 0.02 \\  & T2 WI & 0.49 \(\pm\) 0.12 & 0.73 \(\pm\) 0.11 & 0.50 \(\pm\) 0.12 & 0.73 \(\pm\) 0.05 \\ \hline \multicolumn{2}{c}{\(p\)-value} & 0.0015 & 0.0137 & 0.2097 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Precision and recall for classification. GS, Gleason score; T2 WI, T2-weighted images; ADCmap, apparent diffusion coefficient map diffusion. professionals, with an increased emphasis on interpreting and implementing AI-generated data in patient care. The present study proposed a non-invasive GS classification method, classifying GS as either \(\geq\)7 or \(<\)7, utilizing semantic segmentation and a 3D-CNN. This approach aimed to address the challenges and limitations associated with current diagnostic methods. In prostate outcome studies, GS and its associated Gleason Grade Group have consistently remained the most powerful prognostic predictors, often influencing treatment decisions. However, the GS is currently determined based on pathological diagnoses that require invasive biopsies and has been observed to demonstrate poor reproducibility. There is an inherent discordance between the inter- and intra-observer variability of pathologists, primarily due to the subjectivity of the GS. This variability can lead to discrepancies in diagnosis and treatment, potentially impacting patient outcomes. This observation underscores the need for more objective and reproducible methods of GS determination. In response to this, [PERSON] et al. proposed a deep learning system for GSs of whole-slide prostatectomy images [31]. Their results revealed a significantly higher diagnostic accuracy in deep learning (0.70), which showed a trend toward improved patient risk stratification in correlation with clinical follow-up data. The potential of deep learning system applications to improve the accuracy of GSs without observer dependence is compelling. The ability of such systems to analyze vast amounts of data and identify subtle patterns that may be missed by human observers could revolutionize prostate cancer diagnosis. However, the implementation of these systems is not without challenges and considerations. The integration of AI into clinical practice must be performed thoughtfully, with a clear understanding of its potential impact on patient care, professional roles, and healthcare systems more broadly. Several groups have attempted to use AI-based approaches for non-invasive GS estimation of prostate cancer. [PERSON] et al.