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KMID : 1036820220270030703
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Communication Sciences & Disorders
2022 Volume.27 No. 3 p.703 ~ p.722
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Applications and Performances of Artificial Intelligence in Assessment and Diagnosis of Communication Disorders: A Systematic Review of the Literatures
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Kang Hye-Won
Kang Jin-Kyong
Lee Soo-Bok
Sim Hyun-Sub
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Abstract
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Objectives : A systematic review of the literature was undertaken (1) to investigate research trends on how artificial intelligence is being used for assessment and diagnosis in the field of communication disorders and (2) to suggest consideration and a directions for the effective use of artificial intelligence in clinical settings.
Methods : A total of 328 articles published in foreign journals between January 2016 and August 2021 were searched using 6 databases and a manual search, and 18 articles were finally selected according to PICO strategy (Population, Intervention, Comparison, Outcome) inclusion and exclusion criteria. Four authors determined the report selection and data extraction. They also independently analyzed the quality of the selected papers using QUADAS-II (Quality Assessment of Diagnostic Accuracy Studies-II).
Results : Firstly, the selected studies had a generally low risk of bias. Secondly, the major subjects of studies were children with communication disorders. Thirdly, most of the studies included in the analysis were experimental studies to verify the effectiveness of using artificial intelligence. Lastly, the extracted features for assessment and diagnosis were biased against acoustic features at the levels of phoneme and word in speaking tasks. The performance of artificial intelligence in the selected studies differed according to the research purpose and evaluation metrics.
Conclusion : This study suggests that in order for artificial intelligence to be used in the assessment and diagnosis system, it is essential to acquire clinically reliable and high-quality big data on the characteristics of speech and language of people with communication disorders.
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KEYWORD
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Communication disorders, Assessment and diagnosis, Artificial intelligence (AI), Machine learning, Deep learning, Convergence research
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