ReviewMovement disorders clinical practice2026
Characteristics and Validity of Commercially Available Technologies Analyzing Voice Features to Assess Parkinson's Disease.
Review in Movement disorders clinical practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInterest in technologies for quantitative assessment of Parkinson's disease (PD) is growing, particularly those enabling voice and speech analysis. However, clinical validation varies widely, much algorithm data remain unpublished, and real-world use is still limited.
objectiveThe aim was to provide an overview of the characteristics and validity of commercially available tools that utilize machine learning to assess voice and speech features for identifying PD or assessing disease severity.
methodsThe literature was reviewed to (1) identify, (2) compare, and (3) group commercially available tools deployed in smartphone apps and other scalable platforms based on their (1) previous use in PD, (2) feasibility, and (3) successful clinimetric testing. The International Parkinson and Movement Disorder Society Digital Tools and Technologies Repository; online databases, for example, PubMed; and web pages related to the entities associated with different technologies were searched for tools that use voice assessment. Identified devices were grouped into 3 categories: (1) "recommended," (2) "suggested," or (3) "listed."
resultsTwelve relevant technologies were identified. One was placed in the "recommended" group ("Dysarthria Analyzer"), 6 in the "suggested" group, ("Audeering," "Aural Analytics," Canary Speech," "KI:Elements," "Modality.ai," and "Redenlab"), and 5 in the "listed" group ("i-Prognosis," "No Pa," and "PdAssist," as well as "mPower" and "HopkinsPD").
conclusionsDespite their potential for clinical and research applications, their suitability in real-world environments remains to be determined.
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