## 5 Challenges and Perspectives  
The perspectives opened by the AI approaches are huge and represent the main promise and challenge for radiology in the next years. However, when dealing with the creation and diffuse adoption of AI-based software in clinical practice, some difficulties should be considered. Developing AI-based tools takes a multidisciplinary team effort and knowledge, large amounts of high-quality data, and a rigorous workflow. Access to a large amount of data is essential for the development of reliable AI approaches [122]: large datasets for training, validating, and testing these models are essential. This problem has been partially addressed in recent years by the rising availability of open-source image repositories and data sharing. Large dataset requirements may also encourage cross-institutional collaboration in research. Another significant problem relates to the so-called \"black box\" phenomenon and the difficulty in understanding results from AI-based models [24; 123]. Executing complex tasks requires the use of complex models that operate at a level of abstraction that makes it challenging to fully comprehend how they interact with the characteristics that radiologists typically classify. For instance, \"decision tree\" type models have high interpretability because the algorithm on which they are based can be broken down and understood when used to operate a classification task. However, they may perform inaccurately when used to operate more complex tasks. On the other hand, AI models such as Support Vector Machines or CNN which are considered particularly suitable for imaging tasks, show great accuracy at the expense of less interpretability. Model validation is a critical step in the creation of ML models. The data used to train the model in the real world represents a sample of the population that may or may not be fully representative. When used outside of the training set, a model's performance might degrade if the sample is not sufficiently representative. This is where validation, which is necessary and not optional, comes into play. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Authors** & **Object of Analysis** & **Dataset** & **AGE** & **CNN** & **MAE** & **Accuracy (\%)** & **RMSE (Years)** \\ \hline [PERSON] et al. [112] & Elbow radiographs & 576 & adolescents & U-Net, RPN+, F-RCNN, VGC16 & 2.8 months & & \\ \hline [PERSON] et al. [113] & Knee MRI & 589 & 13–21 & N4 ITK, MAdM, U-Net, AgeNet2D & 0.67 \(\pm\) 0.49 y & 90.9 & \\ \hline [PERSON] et al. [114] & Knee MRI & 402 & 14–21 & GoogleNet, ResNet-50, Inception-v3, VGG, AlexNet, DenseNet, U-Net & 0.793–0.988 y & 95–98.1 & \\ \hline [PERSON] et al. [115] & MRI of hands, clavicle, and teeth & 322 & 13–25 & Inception V3 & 1.01 \(\pm\) 0.74 y & & \\ \hline [PERSON] at al. [117] & Cervical vertebrae in lateral cephalograms & 499 & 6–18 & BayesianRidge, Ridge, LinearRegression, HuberRegressor, SGDRegressor, RandronForestRegressors, TheilenRegressor, AdaBoostRegressor and LinearSV & 0.9 y & 1.2 \\ \hline [PERSON] et al. [118] & Pelvic radiographs & 962 & 11–21 & Inception-V3, Inception-ResNet-V2, and VGG19 & 0.82–1.02 y & 1.11–1.29 \\ \hline [PERSON] et al. [119] & Pelvic radiographs & 2164 & 11–21 & Inception-V3, Inception-ResNet-V2, and VGG19, U-Net & 0.93–1.14 y & 1.22–1.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The available AI applications for bone age estimation based on the analysis of skeletal segments different from hand–wrist. Among the many models, random sampling, leave-one-out cross-validation, k-fold cross-validation, and bootstrapping are some of the most popular. Choosing the best model is influenced by several variables. Focusing on AI-based bone age-assessment methods, the most important problem is the application to people of various ethnic and socioeconomic backgrounds, as it is well-known that different populations show different rates of skeletal maturation [124].