Abstract
OBJECTIVES: This study aims to fulfill the current research gap pertaining to the lack of a comprehensive systematic mapping study (SMS) regarding the effectiveness of artificial intelligence (AI) machine learning algorithms in the diagnosis of speech and language disorders (SLDs), and the extent to which such AI algorithms can automate this diagnostic process. METHODS: An SMS has been implemented following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines; 19,774 research papers were screened resulting in 70 studies meeting purposely designed inclusion and exclusion criteria. RESULTS: The findings revealed multiple research gaps including substantial divide in the application of AI machine learning algorithms for diagnosing SLDs, where 91.43% versus 8.57% of the studies relating to SLDs, respectively. This is further exacerbated by the absence AI machine learning algorithms for diagnosing prevalent language disorders, such as developmental language disorders in children. Furthermore, most AI machine learning algorithms for diagnosing SLDs are focused on binary classification of these disorders, for example, healthy and pathological voices, but not providing detailed diagnostics, such as the impaired aspects and contextual SLDs severity. Finally, AI machine learning algorithms have predominantly focused on partially automating the SLD assessment phase of the diagnostic process (76%) compared to those that have extended the automation to partially include the diagnosis determination phase (24%). CONCLUSION: The effectiveness of AI machine learning algorithms in automating SLD diagnosis cannot be claimed without larger population datasets, highlighting a research gap for developing AI models to automate the four phases of the SLD diagnostic process and link them to treatment protocols in clinical settings.