## 3 Results
### AI Models  
Properties of the AI models studied can be found in Tables 2 and 3. The developed models primarily focused on classification, followed by segmentation and localization; see Figure 5. Six studies applied ML models for either classification (n = 5) or segmentation (n = 1) tasks. Fourteen studies applied DL models for classification (n = 12), segmentation (n = 1), or localization (n = 1) tasks. Prominent DL architectures for classification were VGG-16, DenseNets, and ResNets. The predicted classes were compared to labels originating from (histo)pathology reports (n = 8), clinical diagnosis (n = 8), or expert annotations (n = 2). Four studies encompassed multiclass tasks where models predicted multiple malignancy risk-level descriptors [41; 43; 46; 47]. The average reported classification accuracy was 0.89 (0.78-0.95) across binary models and 0.85 (0.78-0.96) across multiclass models (Table 2). The highest performing models combined either DenseNets [41] or ResNet-18 [47] with an attention module for final class prediction. Segmentation tasks were performed using a U-Net architecture in one out of two studies [43], with a reported Dice similarity coefficient (DSC) of 0.83 (Table 3). The localization task was performed on a global level. The results from segmentation and localization tasks were compared to ground truth manual annotation masks. \begin{table}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|l|l|} \hline
**Author (Year)** & **Cancer Type** & **Clinical** & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} & \multicolumn{1}{c|}{**Single**} \\ \hline Al-pleplem1 A.H. & Thyroid & Diagnosis & R & US & Continuous & STNU & 1810 (2086) & 4136 & - & 1884 (45.6\%) & 2252 (54.4\%) \\ (2023) [57] & \multicolumn{1}{c}{} & & & & & & & & & & & & \\ \hline Bensu & \multicolumn{1}{c}{} & & & & & & & & & & & & & \\ (2023) [8] & Gallbladder & Diagnosis & R & US & Single shot & GRUS & 218 (147) & 1255 & 432 (34.4\%) & 558 (44.5\%) & 265 (21.1\%) \\ \hline [PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ (2022) [9] & Breast & Diagnosis & R & US & Single shot & Clinical & NA (272) & 272 & - & 149 (54.8\%) & 122 (45.2\%) \\ \hline [PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ (2021) [16] & Breast & Diagnosis & R & US & Continuous & Clinical & 367 (785) & 579 & - & 247 (42.7\%) & 332 (57.3\%) \\ (2021) [16] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ \hline [PERSON] & Thyroid & Diagnosis & P & US & Single & Clinical & 3906 (900) & 3906 & - & 1696 (43.4\%) & 2210 (56.6\%) \\ (2022) [41] & \multicolumn{1}{c}{} & & & & & & & & & & \\ \[PERSON] & Prostate & Diagnosis & R & US + MRI & (US) + Statec & Imaging & 1151 (NA) & 611,119 & - & NA & NA \\ (2023) [42] & \multicolumn{1}{c}{} & & & & & & & & & & \\ \hline [PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ (2023) [43] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ \hline [PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ (2021) [16] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ \hline [PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & & \\ (2023) [44] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ \[PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & & \\ (2021) [47] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ (2021) [47] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ \[PERSON], [PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & & \\ (2022) [6] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ \[PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & & \\ (2023) [49] & \multicolumn{1}{c}{} & & & & & & & & & & & \\ \hline [PERSON] & \multicolumn{1}{c}{} & & & & & & & & & & & & \\ (2024) [50] & \multicolumn{1}{c}{} & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: General characteristics of included studies.