## 9 Key Observations and Recommendations
### Key Observations  
Based on the literature review of the period from 2009 to 2024 and the collected empirical data, key trends and patterns in the application of AI in dermatology can be identified. The following observations highlight the dominant approaches, the most commonly used techniques, and the primary challenges that researchers encounter when designing and evaluating models that learn from skin images. This will provide the reader with a synthetic overview of the most important findings, which are the foundation for further recommendations.  
* Dominance of CNNs in diagnostic imaging. Most works (\(\sim\)65%) use convolutional neural networks, with transfer learning architectures (e.g., ResNet, DenseNet) being the most common.
* Emergence of Transformer-based models. Recent studies have introduced Vision Transformer architectures (e.g., Swin Transformer, ViT variants) that achieve state-of-the-art accuracy in skin lesion classification, albeit with substantially higher computational and data requirements. This marks a new trend that challenges the CNN's dominance.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Number of Images** & **Number of Classes** & **Evaluation Measure** & **Main Limitations** \\ \hline _ISIC (2019)_ & 25,331 train + 8239 test & 8 (+OOD) & Balanced accuracy & \begin{tabular}{l} Class imbalance; limited phototype \\ diversity; partial lack of histopathology \\ \end{tabular} \\ \hline _HAM10000_ & 10,015 & 7 & Balanced accuracy & \
\begin{tabular}{l} Class imbalance; no images outside the \\ pigmented lesion range; \(\sim\)50\% without \\ histopathological confirmation \\ \end{tabular} \\ \hline _BCN20000_ & 18,946 (train) & 8 (+OOD) & Balanced accuracy &
\begin{tabular}{l} Class imbalance; one site (geographic/skin \\ bias); limited phototype data \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of sets of dermoscopic skin images: ISIC 2019, HAM10000, BCN20000. In BCN20000, the number of training images is given; the test set (20% of the total) contains an additional OOD class. The _balanced accuracy_ measure denotes the arithmetic mean of sensitivity (TPR) obtained separately for each class, which prevents the dominant class from overestimating the effectiveness. Limitations include set bias (e.g., dominance of light skin phototypes) and annotation quality (e.g., lack of routine confirmation of each diagnosis by histopathological examination).  
* Adoption of ensemble learning for performance gains. A few works combine multiple models to boost the accuracy--for example, ensembling lightweight CNNs (as in SkinNet) significantly improved the classification metrics (achieving AUC \(\approx\) 0.96). Such ensembles leverage complementary strengths at the cost of greater complexity.
* Growing role of data augmentation. The use of GAN-generated images and augmented imaging (rotation, scaling, colour shifts) has become common, improving the classification accuracy, on average, by 3-7%. It helps to address data scarcity and imbalances in training sets.
* Persistent class imbalance challenges. Skewed datasets (e.g., few melanomas among many nevi) remain problematic. Specialised strategies to rebalance training data (for instance, the approach used in DSCC_Net to equalise class representation) have been shown to markedly enhance the model AUC and sensitivity, underlining the importance of addressing rare lesion classes.
* Insufficient standardisation of evaluations. A lack of uniform protocols for training/test splits and performance metrics (such as cross-validation vs. random splits and varying datasets) makes it difficult to compare results across studies. This variability hampers the objective benchmarking of different AI models in dermatology.
* Limited interpretability of models. Few publications consider model interpretability (e.g., saliency maps like Grad-CAM or LIME), which raises concerns about clinical trust. The black-box nature of many AI models remains a significant obstacle to their acceptance in dermatological practice.