Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review

Transfer learning outperforms other approaches for practical PD diagnosis: The review found that transfer learning (TL) methods provide "more robust PD diagnosis and better generalizability across languages" compared to end-to-end approaches, making them more reliable for real-world clinical deployment across diverse populations.

Privacy concerns are severely limiting AI development in this field: The authors note that progress in speech-based PD research is "restricted by the limited availability of publicly accessible speech-based PD datasets, primarily due to privacy concerns," highlighting a critical tension between protecting patient data and advancing diagnostic technology.

Explainability remains a significant challenge: Deep acoustic feature extraction (DAFE) methods are being developed specifically to "improve the explainability and interpretability of results," indicating that current AI models often function as "black boxes" that clinicians cannot easily understand or trust for medical decision-making.

Transformer models show promise but face practical barriers: While Transformers are "increasingly popular" for speech analysis, the review identifies that they face "challenges such as limited data and computational resources," suggesting that the most advanced AI approaches may not be accessible to all research institutions or healthcare settings.

Bias in AI systems requires urgent attention: The review explicitly discusses "issues related to bias, explainability, and privacy" as key concerns in current research, emphasizing that responsible development requires addressing how these systems may perform differently across patient populations.