## 6 Discussion
### Explainability Issues  
Another issue of the approaches discussed in this review is the explainability and interpretability of the results. Explainable Artificial Intelligence (XAI) is a current trending topic focused on making AI approaches more explainable [8]. Unfortunately, the explainability of all AI approaches discussed in this review tends to be a challenge. Regarding E2E learning, we can simply input the probabilistic representation of the spectrogram of the raw audio signals and receive a classification decision as a black-box output. [PERSON] et al. [21] use Grad-CAM for feature visualization, demonstrating that low-frequency regions have a greater influence than high-frequency regions in identifying PD, as described in Section 3, while various visualization methods exist for CNNs, appropriate visualization techniques for Transformers remain limited [77]. CNNs capture spatial and local patterns, making it generally easier to visualize localized regions using methods like Grad-CAM [37]. In contrast, Transformers rely on self-attention, which is not localized, making it more challenging to create coherent and interpretable visualizations [78]. DAFE approaches may seem more explainable than E2E methods at first glance. However, in practice, we extract acoustic features from one DNN and input them into another DL layer or ML model. We still do not fully understand how these features are extracted or how they contribute to the decision of whether a person has PD. This lack of transparency means these approaches are not truly 'explainable.' [PERSON] et al. [13] utilize both IFMs and NIFMs, with the latter being DNN embeddings extracted using Wav2 Vec2.0, TRILLsson, and HuBERT. The authors demonstrated that NIFMs outperform IFMs and traditional features such as MFCCs. Consequently, researchers will likely continue to prefer non-interpretable features over interpretable ones due to their superior performance. This underscores the importance of future work aimed at developing approaches that enhance explainability. Enhanced explainability is essential for achieving acceptance and credibility in clinical settings [79]. As noted earlier, visualization methods are commonly used to address explainability challenges in E2E approaches, particularly in CNNs. Techniques such as deconvolutional neural networks and Deep Taylor decomposition can generate saliency maps that highlight the most relevant regions of an input image [80]. Layer-wise relevance propagation, which shows the positive or negative contribution of each pixel at every layer, may provide valuable insights for researchers, but the former two methods are likely more interpretable for clinicians. However, visualization methods are not the only solution for improving explainability. In [81], the authors introduce Listenable Maps for Audio Classifiers (L-MAC), which identify relevant audio segments. This approach could be particularly useful for PD diagnosis, as it allows researchers and clinicians to hear which parts of a sentence are most indicative of PD. Audio explanations may even be more intuitive than visual ones obtained with Grad-CAM and similar techniques. Finally, in DAFE approaches, it is important to identify which extracted features contribute most to determining whether a person has PD. Techniques like SHapley Additive exPlanations (SHAP) can assign an importance value to each feature [82], while this insight is valuable, these features are often extracted from a DNN and are less interpretable compared to traditional features like jitter, shimmer, and MFCCs. Understanding the importance of deep features may be interesting for researchers seeking to understand a model's inner workings, but clinicians are likely more interested in the speech-related characteristics that indicate PD. Therefore, feature-importance techniques like SHAP might be less useful for speech-based PD diagnosis.