TY - JOUR
T1 - Clinical Application of Machine Learning in Biomedical Engineering for the Early Detection of Neurological Disorders
AU - Georgiou, Georgios P.
N1 - Publisher Copyright:
© The Author(s) under exclusive licence to Biomedical Engineering Society 2025.
PY - 2025/10
Y1 - 2025/10
N2 - 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.
AB - 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.
KW - Clinical
KW - Language
KW - Machine learning
KW - Neurological disorders
KW - Speech
UR - https://www.scopus.com/pages/publications/105012731228
U2 - 10.1007/s10439-025-03820-0
DO - 10.1007/s10439-025-03820-0
M3 - Letter
C2 - 40773081
AN - SCOPUS:105012731228
SN - 0090-6964
VL - 53
SP - 2389
EP - 2391
JO - Annals of Biomedical Engineering
JF - Annals of Biomedical Engineering
IS - 10
ER -