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<p>Machine learning is increasingly recognized as a transformative tool in the diagnosis and prognosis of neurodevelopmental, neurodegenerative, and&#xa0;learning disorders. Through the analysis of complex patterns in speech and language, these models may offer important insights that can support and enhance clinical decision-making. This paper explores the potential of machine learning to detect a range of disorders and discusses its key advantages, limitations, and clinical integration.</p>

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Clinical Application of Machine Learning in Biomedical Engineering for the Early Detection of Neurological Disorders

  • 期刊论文

  • 发表日期：

    2025年8月7日

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  • Georgios P. Georgiou

摘要

Machine learning is increasingly recognized as a transformative tool in the diagnosis and prognosis of neurodevelopmental, neurodegenerative, and learning disorders. Through the analysis of complex patterns in speech and language, these models may offer important insights that can support and enhance clinical decision-making. This paper explores the potential of machine learning to detect a range of disorders and discusses its key advantages, limitations, and clinical integration.