Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches

Abstract

PURPOSE: Recent advances in artificial intelligence provide opportunities to capture and represent complex features of human language in a more automated manner, offering potential means of improving the efficiency of language assessment. This review article presents computerized approaches for the analysis of narrative language and identification of language disorders in children. METHOD: We first describe the current barriers to clinicians' use of language sample analysis, narrative language sampling approaches, and the data processing stages that precede analysis. We then present recent studies demonstrating the automated extraction of linguistic features and identification of developmental language disorder using natural language processing and machine learning. We explain how these tools operate and emphasize how the decisions made in construction impact their performance in important ways, especially in the analysis of child language samples. We conclude with a discussion of major challenges in the field with respect to bias, access, and generalizability across settings and applications. CONCLUSION: Given the progress that has occurred over the last decade, computer-automated approaches offer a promising opportunity to improve the efficiency and accessibility of language sample analysis and expedite the diagnosis and treatment of language disorders in children.

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