# Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey

arXiv: https://arxiv.org/abs/2502.10703 · HTML: https://mlsanity.com/paper/2502.10703/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 137 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Learning-Based Gait Recognition Using Smartphones in the Wild](https://mlsanity.com/paper/1811.00338/) (2018)
- [Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait](https://mlsanity.com/paper/1910.11509/) (2019)
- [Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity](https://mlsanity.com/paper/2007.08920/) (2020)
- [Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition](https://mlsanity.com/paper/2204.01670/) (2022)
- [Computer keyboard interaction as an indicator of early Parkinson's disease](https://mlsanity.com/paper/1604.08620/) (2016)
- [Multimodal Gait Recognition for Neurodegenerative Diseases](https://mlsanity.com/paper/2101.02469/) (2021)
- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)
- [Associated Spatio-Temporal Capsule Network for Gait Recognition](https://mlsanity.com/paper/2101.02458/) (2021)
- [Characterization of the Handwriting Skills as a Biomarker for Parkinson Disease](https://mlsanity.com/paper/1903.08226/) (2019)
- [1D-Convolutional transformer for Parkinson disease diagnosis from gait](https://mlsanity.com/paper/2311.03177/) (2023)
- [Transformers for 1D Signals in Parkinson's Disease Detection from Gait](https://mlsanity.com/paper/2204.00423/) (2022)
- [HCT: Hybrid Convnet-Transformer for Parkinson's disease detection and severity prediction from gait](https://mlsanity.com/paper/2310.17078/) (2023)
- [Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNet](https://mlsanity.com/paper/2406.17825/) (2024)
- [A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers](https://mlsanity.com/paper/2309.09329/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities](https://mlsanity.com/paper/2505.00525/) (2025)
- [BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech](https://mlsanity.com/paper/2505.12192/) (2025)

## Abstract (arXiv)

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.