{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T18:24:17Z","timestamp":1772994257761,"version":"3.50.1"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["movementdisorders.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Movement Disord Clin Pract"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Interest 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 However, clinical validation varies widely, much algorithm data remain unpublished, and real\u2010world use is still limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The 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) \u201crecommended,\u201d (2) \u201csuggested,\u201d or (3) \u201clisted.\u201d<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Twelve relevant technologies were identified. One was placed in the \u201crecommended\u201d group (\u201cDysarthria Analyzer\u201d), 6 in the \u201csuggested\u201d group, (\u201cAudeering,\u201d \u201cAural Analytics,\u201d Canary Speech,\u201d \u201cKI:Elements,\u201d \u201cModality.ai,\u201d and \u201cRedenlab\u201d), and 5 in the \u201clisted\u201d group (\u201ci\u2010Prognosis,\u201d \u201cNo Pa,\u201d and \u201cPdAssist,\u201d as well as \u201cmPower\u201d and \u201cHopkinsPD\u201d).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Despite their potential for clinical and research applications, their suitability in real\u2010world environments remains to be determined.<\/jats:p>\n                  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