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Platform design reveals automated differentiation of voice pathologies using acoustic parameters, suggesting accessible artificial intelligence integration into clinical practice.
The diagnosis of voice disorders remains a persistent challenge in otorhinolaryngology, as the analysis of complex data demands specialized clinical expertise. A promising approach involves the implementation of artificial intelligence (AI) and machine learning methods, which can automate the analysis of acoustic voice parameters, objectively differentiating between norm and pathology. The creation of user-friendly, secure and accessible cloud-based platforms utilizing environments like Google Drive and Colaboratory reduces the need for expensive infrastructure. This is a critical prerequisite for the successful integration of AI into clinical practice.
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Kurbanova et al. (2026) studied this question.
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