This paper is in the following e-collection/theme issue:

Systematic Review Protocols (336) Artificial Intelligence (2157)

Published on in Vol 12 (2023)

This is a member publication of Imperial College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46581, first published .
Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review

Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review

Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review

Authors of this article:

Katie Bounsall1 Author Orcid Image ;   Madison Milne-Ives2 Author Orcid Image ;   Andrew Hall1 Author Orcid Image ;   Camille Carroll1, 3 Author Orcid Image ;   Edward Meinert2, 4, 5, 6 Author Orcid Image
Article Authors Cited by (13) Tweetations (4) Metrics

Journals

  1. Kuroiwa T, Sarcon A, Ibara T, Yamada E, Yamamoto A, Tsukamoto K, Fujita K. The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study. Journal of Medical Internet Research 2023;25:e47621 View
  2. Jadhwani P, Harjpal P. A Review of Artificial Intelligence-Based Gait Evaluation and Rehabilitation in Parkinson’s Disease. Cureus 2023 View
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  5. Boege S, Milne-Ives M, Meinert E, Carroll C. Self-Management Systems for Patients and Clinicians in Parkinson Care: Protocol for an Integrated Scoping Review, Product Search, and Evaluation. JMIR Research Protocols 2024;13:e58845 View
  6. Omkar , Anith , Pratik , Sushmitha , Sruthi Dinesh . Diagnosis of Parkinson's: A Novel Approach. International Journal of Advanced Research in Science, Communication and Technology 2024:125 View
  7. Ducange P, Marcelloni F, Renda A, Ruffini F. Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease. Cognitive Computation 2024;16(6):3051 View
  8. Boege S, Milne-Ives M, Ananthakrishnan A, Carroll C, Meinert E. Self-Management Systems for Patients and Clinicians in Parkinson’s Disease Care: A Scoping Review. Journal of Parkinson’s Disease 2024;14(7):1387 View
  9. Onciul R, Tataru C, Dumitru A, Crivoi C, Serban M, Covache-Busuioc R, Radoi M, Toader C. Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications. Journal of Clinical Medicine 2025;14(2):550 View
  10. Malaguti M, Longo C, Moroni M, Ragni F, Bovo S, Chierici M, Gios L, Avanzino L, Marchese R, Di Biasio F, Pardini M, Cerne D, Mandich P, Marenco M, Uccelli A, Giometto B, Jurman G, Osmani V. Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study. European Journal of Neurology 2025;32(5) View
  11. Ferrara F. Parkinson's disease between old drugs and new digital therapies: A pharmacological review. NPG Neurologie - Psychiatrie - Gériatrie 2025;25(148):179 View
  12. Ianculescu M, Băjenaru L, Vasilevschi A, Gheorghe-Moisii M, Gheorghe C. Advancing Neurodegenerative Disease Management: Technical, Ethical, and Regulatory Insights from the NeuroPredict Platform. Future Internet 2025;17(7):320 View

Conference Proceedings

  1. Mathkunti N, Ananthanagu U, P M E. 2024 IEEE 9th International Conference for Convergence in Technology (I2CT). Brain Disease Parkinson's Diagnosis using VGG-16 and VGG-19 with Spiral and Waves drawings as Input View