The rapid emergence of highly adaptable and reusable artificial intelligence (AI) models is set to revolutionize the medical field, particularly in the diagnosis and management of Parkinson's disease (PD). Currently, there are no effective biomarkers for diagnosing PD, assessing its severity, or tracking its progression. Numerous AI algorithms are now being used for PD diagnosis and treatment, capable of performing various classification tasks based on multimodal and heterogeneous disease symptom data, such as gait, hand movements, and speech patterns of PD patients. They provide expressive feedback, including predicting the potential likelihood of PD, assessing the severity of individual or multiple symptoms, aiding in early detection, and evaluating rehabilitation and treatment effectiveness, thereby demonstrating advanced medical diagnostic capabilities. Therefore, this work provides a surveyed compilation of recent works regarding PD detection and assessment through biometric symptom recognition with a focus on machine learning and deep learning approaches, emphasizing their benefits, and exposing their weaknesses, and their impact in opening up newer research avenues. Additionally, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints. Furthermore, the paper explores the potential opportunities and challenges presented by data-driven AI technologies in the diagnosis of PD.

翻译：高度适应性和可复用人工智能（AI）模型的迅速涌现，有望彻底改变医学领域，尤其是在帕金森病（PD）的诊断与管理方面。目前，尚无有效的生物标志物可用于诊断PD、评估其严重程度或追踪其进展。众多AI算法正被用于PD的诊断与治疗，这些算法能够基于多模态、异质性的疾病症状数据（例如PD患者的步态、手部运动和语音模式）执行各种分类任务。它们提供富有表现力的反馈，包括预测PD的潜在可能性、评估单个或多个症状的严重程度、辅助早期检测以及评估康复与治疗效果，从而展现出先进的医疗诊断能力。因此，本文通过生物特征症状识别，对近期关于PD检测与评估的研究工作进行了综述性汇编，重点关注机器学习和深度学习方法，强调其优势，揭示其不足，并探讨其在开辟新研究途径方面的影响。此外，本文还对用于应对相关约束的数据集、方法与架构进行了分类和特征描述。最后，本文探讨了数据驱动的AI技术在PD诊断中带来的潜在机遇与挑战。