## 5 Discussion  
### Main Findings and Limitations  
From this review, it emerges that machine learning algorithms have improved diagnostic accuracy by providing an excellent tool for the identification of anomalies in whole slide images (WSIs) that may be missed, in some cases, by human pathologists. For example, recent studies have demonstrated improved accuracy in tumor grading and biomarker scoring, such as PD-L1 and CD8+ cell density measurements in cancer tissues [36; 37]. The adoption of these tools drastically reduced the time for manual review, allowing pathologists to focus on those cases that are inherently more complex [37; 38].  
The most important aspect concerns the fact that machine learning algorithms simplified the quantitative analysis of complex histopathological images, also providing more consistent and reproducible results and reducing inter-observer variability [38; 39]. Finally, these tools have shown good performance in detecting and classifying various cell types and tissues, which is of paramount importance for understanding diseases and their progression. Despite these undeniable positive aspects, there is a list of limitations that need to be discussed.  
First, the performance of machine learning algorithms is strongly affected by the quality and quantity of data. Obtaining such big datasets can be challenging because of manual annotation (which is time-consuming) and variability in image quality. Moreover, fordeep learning approaches, high-performance computing infrastructures are an important prerequisite, and this issue can represent an obstacle for several institutions.  
Second, there are issues related to the need for pathologists to adapt to new technologies despite the fact that these tools may indirectly hinder traditional clinical practices. In our opinion, considering that the internal mechanism of these tools is usually hidden from the user, the most relevant limitation is probably related to interpretability, which makes it difficult to understand how final decisions are made [68]. In fact, in some machine learning systems, such as linear models and decision trees, it can be relatively straightforward to explain the algorithm's outcomes based on the image features used. However, the most effective algorithms for medical image analysis today are often based on deep learning models. These models are considered \"black boxes\" because their decision-making processes are typically hidden (even to their developers). Even the specific image features extracted by these algorithms are not always clear, making it difficult to understand why a particular decision was made. This lack of interpretability is particularly concerning in medicine, as it is crucial for the physician to comprehend the rationale behind algorithmic decisions, both to avoid errors and ensure patient safety.  
Third, an effect to consider when using machine learning models, especially deep learning models, is system bias. Bias in deep learning models refers to the tendency to produce skewed or unfair outcomes because of underlying biases in the training data. This effect can result from unbalanced or non-representative training data, leading to unequal performance in medical image characterization and potentially exacerbating healthcare disparities. A typical example is when a demographic group is overrepresented in the training dataset, causing unequal performance across different demographic groups and further contributing to healthcare disparities [69; 70].