Review
Artificial intelligence in assessment and intervention of speech and language disorders: A literature review
Abstract
Artificial intelligence (AI) is a broad term that refers to the use of computers to replicate intelligent behavior with minimal human intervention. AI is rapidly transforming various sectors, including speech and language pathology, by offering innovative solutions to enhance therapeutic practices and client outcomes. Its application in speech and language pathology spans several domains, including medical diagnosis, therapeutic planning, and rehabilitation, utilizing tools such as machine learning and deep learning to enhance data analysis and pattern recognition. The primary aim of this study is to provide resources for speech and language pathologists on the topic of artificial intelligence by presenting research findings on the assessment and intervention of speech and language disorders using AI. Accordingly, AI studies in speech and language pathology found in the literature were included. The results of these studies were summarized, and information was provided on the use of AI in assessing and treating speech and language disorders, including swallowing disorders, voice disorders, acquired language disorders, motor and speech sound disorders, cleft palate speech, and developmental language disorder. Existing literature acknowledges and supports the growing popularity of AI and AI-based algorithms in speech and language pathology. Although the current evidence remains insufficient and concerns about ethics and implementation persist, advancing technology offers promise for applying AI in this field.
Keywords
Artificial intelligence speech and language pathology speech and language pathologist voice disorders speech disorders
Ethical Statement
Ethical approval is not required for this study. There are no human or animal elements in the study. This review was carried out by a brief literature screening.
References
- 1. Morandín-Ahuerma F. What is Artificial Intelligence? International Journal of Research Publication and Reviews. 2022;3(12):1947-1951. doi: 10.55248/gengpi.2022.31261.
- 2. Villamil V, Deloria R, Wolbring G. Artificial intelligence and machine learning: What is the role of social workers, occupational therapists, audiologists, nurses and speech language pathologists according to academic literature and Canadian newspaper coverage? In: ACM International Conference Proceeding Series. Association for Computing Machinery; 2019:83-86. doi: 10.1145/3364138.3364158.
- 3. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88. doi: 10.1016/j.media.2017.07.005.
- 4. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. doi: 10.1136/svn-2017-000101.
- 5. Duffy JR. Motor Speech Disorders: Where Will We Be in 10 Years? Semin Speech Lang. 2016;37(3):219-224. doi: 10.1055/s-0036-1584154.
- 6. ASHA. How Will Artificial Intelligence Reshape Speech-Language Pathology Services and Practice in the Future? 2020. (access date: 13.04.2024) https://academy.pubs.asha.org/2020/08/how-will-artificial-intelligence-reshape-speech-language-pathology-services-and-practice-in-the-future.
- 7. Suh H, Dangol A, Meadan H, Miller CA, Kientz JA. Opportunities and Challenges for AI-Based Support for Speech-Language Pathologists. In: ACM International Conference Proceeding Series. Association for Computing Machinery. 2024. doi: 10.1145/3663384.3663387.
- 8. Aggarwal K, Ravi R, Yerraguntla K. Artificial Intelligence and Patient Care: Perspectives of Audiologists and Speech-Language Pathologists. Intell Based Med. 2025;11:100214. doi: 10.1016/j.ibmed.2025.100214.
- 9. Zhang B, Wong KP, Liu M, et al. Effect of artificial intelligence-based video-game system on dysphagia in patients with stroke: A randomized controlled trial. Clin Nutr. 2025;45:81-90. doi: 10.1016/j.clnu.2024.12.022.
- 10. Hou M, Zhao Y, Zhao L, Yuan X, Liu Z, Li H. Efficacy of game training combined with surface electromyography biofeedback on post-stroke dysphagia. Geriatr Nurs (Minneap). 2024;55:255-262. doi: 10.1016/j.gerinurse.2023.11.019.
- 11. Jeong CW, Lee CS, Lim DW, et al. The Development of an Artificial Intelligence Video Analysis-Based Web Application to Diagnose Oropharyngeal Dysphagia: A Pilot Study. Brain Sci. 2024;14(6):546. doi: 10.3390/brainsci14060546.
- 12. Barron K, Blaivas M, Blaivas L, Sadler J, Deal I. Bedside Ultrasound to Identify and Predict Severity of Dysphagia Following Ischemic Stroke: Human Versus Artificial Intelligence. Ultrasound Med Biol. 2024;50(1):99-104. doi: 10.1016/j.ultrasmedbio.2023.09.008.
- 13. Sanjeevi G, Gopalakrishnan U, Pathinarupothi RK, Iyer KS. Artificial Intelligence in Videofluoroscopy Swallow Study Analysis: A Comprehensive Review. Dysphagia. 2025 Feb 17. doi: 10.1007/s00455-025-10812-8.
- 14. Kim JK, Choo YJ, Choi GS, Shin H, Chang MC, Park D. Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study. J Korean Med Sci. 2022;37(6):e42. doi: 10.3346/jkms.2022.37.e42.
- 15. Iida Y, Näppi J, Kitano T, Hironaka T, Katsumata A, Yoshida H. Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning. Oral Radiol. 2023;39(3):553-562. doi: 10.1007/s11282-023-00669-8.
- 16. Bandini A, Steele CM. The effect of time on the automated detection of the pharyngeal phase in videofluoroscopic swallowing studies. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Institute of Electrical and Electronics Engineers Inc.; 2021:3435-3438. doi: 10.1109/EMBC46164.2021.9629562.
- 17. Hsiao MY, Weng CH, Wang YC, et al. Deep Learning for Automatic Hyoid Tracking in Videofluoroscopic Swallow Studies. Dysphagia. 2023;38(1):171-180. doi: 10.1007/s00455-022-10438-0.
- 18. Nakamori M, Ishikawa R, Watanabe T, et al. Swallowing sound evaluation using an electronic stethoscope and artificial intelligence analysis for patients with amyotrophic lateral sclerosis. Front Neurol. 2023;14:1212024. doi: 10.3389/fneur.2023.1212024.
- 19. Girardi AM, Cardell EA, Bird SP. Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech-Language Pathologists. Big Data Cog Comput. 2023;7(4):178. doi: 10.3390/bdcc7040178.
- 20. Liu GS, Jovanovic N, Sung CK, Doyle PC. A Scoping Review of Artificial Intelligence Detection of Voice Pathology: Challenges and Opportunities. Otolaryngol Head Neck Surg. 2024;171(3):658-666. doi: 10.1002/ohn.809.
- 21. Fujimura S, Kojima T, Okanoue Y, et al. Classification of Voice Disorders Using a One-Dimensional Convolutional Neural Network. J Voice. 2022;36(1):15-20. doi: 10.1016/j.jvoice.2020.02.009.
- 22. Verde L, De Pietro G, Alrashoud M, Ghoneim A, Al-Mutib KN, Sannino G. Leveraging Artificial Intelligence to Improve Voice Disorder Identification Through the Use of a Reliable Mobile App. IEEE Access. 2019;7:124048-124054. doi: 10.1109/ACCESS.2019.2938265.
- 23. Ur Rehman M, Shafique A, Azhar QUA, Jamal SS, Gheraibia Y, Usman AB. Voice disorder detection using machine learning algorithms: An application in speech and language pathology. Eng Appl Artif Intell. 2024;133:108047. doi: 10.1016/j.engappai.2024.108047.
- 24. Costantini G, Cesarini V, Di Leo P, et al. Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison. Sensors (Basel). 2023;23(4):2293. doi:10.3390/s23042293.
- 25. Hegde S, Shetty S, Rai S, Dodderi T. A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders. J Voice. 2019;33(6):947.e11-33. doi: 10.1016/j.jvoice.2018.07.014.
- 26. Al-Hussain G, Shuweihdi F, Alali H, Househ M, Abd-Alrazaq A. The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis. J Med Internet Res. 2022;24(10):e38472. doi: 10.2196/38472.
- 27. Hu HC, Chang SY, Wang CH, et al. Deep learning application for vocal fold disease prediction through voice recognition: Preliminary development study. J Med Internet Res. 2021;23(6):e25247. doi:10.2196/25247.
- 28. Kim HB, Song J, Park S, Lee YO. Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis. Sci Rep. 2024;14(1):9297. doi: 10.1038/s41598-024-58817-x.
- 29. Idrisoglu A, Dallora AL, Anderberg P, Berglund JS. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. J Med Internet Res. 2023;25:46105. doi: 10.2196/46105.
- 30. Cordella C, Marte MJ, Liu H, Kiran S. An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications. Perspect ASHA Spec Interest Groups. 2024;10(2):432-450. doi: 10.1044/2024_PERSP-24-00037.
- 31. Jothi KR, Sivaraju SS, Yawalkar PJ. AI based Speech Language Therapy using Speech Quality Parameters for Aphasia Person: A Comprehensive Review. In: Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020. Institute of Electrical and Electronics Engineers Inc.; 2020:1263-1271. doi: 10.1109/ICECA49313.2020.9297591.
- 32. de la Fuente Garcia S, Ritchie CW, Luz S. Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review. J Alzheimers Dis. 2020;78(4):1547-1574. doi: 10.3233/JAD-200888.
- 33. Grasemann U, Peñaloza C, Dekhtyar M, Miikkulainen R, Kiran S. Predicting language treatment response in bilingual aphasia using neural network-based patient models. Sci Rep. 2021;11(1):10497. doi: 10.1038/s41598-021-89443-6.
- 34. Pustina D, Coslett HB, Ungar L, et al. Enhanced estimations of post-stroke aphasia severity using stacked multimodal predictions. Hum Brain Mapp. 2017;38(11):5603-5615. doi: 10.1002/hbm.23752.
- 35. Kristinsson S, Zhang W, Rorden C, et al. Machine learning-based multimodal prediction of language outcomes in chronic aphasia. Hum Brain Mapp. 2021;42(6):1682-1698. doi: 10.1002/hbm.25321.
- 36. Themistocleous C, Ficek B, Webster K, den Ouden DB, Hillis AE, Tsapkini K. Automatic Subtyping of Individuals with Primary Progressive Aphasia. J Alzheimers Dis. 2021;79(3):1185-1194. doi: 10.3233/JAD-201101.
- 37. Azevedo N, Kehayia E, Jarema G, Le Dorze G, Beaujard C, Yvon M. How artificial intelligence (AI) is used in aphasia rehabilitation: A scoping review. Aphasiology. 2024;38(2):305-336. doi: 10.1080/02687038.2023.2189513.
- 38. Bilkova Z, Novozamsky A, Bartos M, et al. Human computer interface based on tongue and lips movements and its application for speech therapy system. In: Proceeding of IS and T International Symposium on Electronic Imaging: Visualization and Data Analysis. 2020: pp. 389.1-5. doi: 10.2352/ISSN.2470-1173.2020.1.VDA-389.
- 39. Frieg H, Muehlhaus J, Ritterfeld U, Bilda K. ISi-Speech: A Digital Training System for Acquired Dysarthria. Stud Health Technol Inform. 2017;242:330-334. doi: 10.3233/978-1-61499-798-6-330.
- 40. Saz O, Yin SC, Lleida E, Rose R, Vaquero C, Rodríguez WR. Tools and Technologies for Computer-Aided Speech and Language Therapy. Speech Commun. 2009;51(10):948-967. doi: 10.1016/j.specom.2009.04.006.
- 41. Ballard KJ, Etter NM, Shen S, Monroe P, Tien Tan C. Feasibility of Automatic Speech Recognition for Providing Feedback During Tablet-Based Treatment for Apraxia of Speech Plus Aphasia. Am J Speech Lang Pathol. 2019;28(2S):818-834. doi: 10.1044/2018_AJSLP-MSC18-18-0109.
- 42. Deka C, Shrivastava A, Abraham AK, Nautiyal S, Chauhan P. AI-based automated speech therapy tools for persons with speech sound disorder: a systematic literature review. Speech Lang Hear. 2025;28(1):2359274. doi:10. 1080/2050571X.2024.2359274.
- 43. Murero M, Vita S, Mennitto A, D’Ancona G. Artificial Intelligence for Severe Speech Impairment: Innovative approaches to AAC and Communication. PSYCHOBIT 2020. Proceedings of the Second Symposium of Psychology-Based Technologies. Naples, Italy, September 28-29, 2020. CEUR Workshop Proceedings. 2020;2730:31. https://ceur-ws.org/Vol-2730/paper31.pdf. (access date: 13.04.2024)
- 44. Zhang A, Pyon RE, Chen K, Lin AY. Speech Analysis of Patients with Cleft Palate Using Artificial Intelligence Techniques: A Systematic Review. Face. 2023;4(3):327-337. doi:10.1177/27325016231187985.
- 45. Beccaluva EA, Catania F, Arosio F, Garzotto F. Predicting developmental language disorders using artificial intelligence and a speech data analysis tool. Hum Comput Interact. 2024;39(1-2):8-42. doi: 10.1080/07370024.2023.2242837.
- 46. El-Hennawy M. The Impact of Artificial Intelligence (AI) in the Assessment and Treatment of Communication Disorders (A Review of Literature). Egypt J Lang Eng. 2024;11(2):36-45. doi: 10.21608/EJLE.2024.303151.1069.
- 47. Koenecke A, Nam A, Lake E, Nudell J, Quartey M, Mengesha Z, Toups C, Rickford JR, Jurafsky D, Goel S. Racial disparities in automated speech recognition. Proc Natl Acad Sci U S A. 2020;117(14):7684-7689. doi: 10.1073/pnas.1915768117.
- 48. Panch T, Mattie H, Atun R. Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health. 2019;9(2):010318. doi: 10.7189/jogh.09.020318.
- 49. Kanwal K, Ibrahim Z, Siddiqui AA. The Convergence of Artificial Intelligence, Digital Therapeutics, and Biomedical Electronics in the field of Speech-Language Therapy. Allied Med Res J. 2024;2(2):277-292. doi: 10.59564/amrj/02.02/030
- 50. Zhang J, Zhang ZM. Ethics and governance of trustworthy medical artificial intelligence. BMC Med Inform Decis Mak. 2023;23(1):7. doi: 10.1186/s12911-023-02103-9.
- 51. Birol NY, Çiftci HB, Yılmaz A, Çağlayan A, Alkan F. Is there any room for ChatGPT AI bot in speech-language pathology? Eur Arch Otorhinolaryngol. 2025;282(6):3267-3280. doi: 10.1007/s00405-025-09295-y.
There are 51 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Allied Health and Rehabilitation Science (Other) |
| Journal Section | Reviews |
| Authors | |
| Early Pub Date | June 21, 2025 |
| Publication Date | |
| Submission Date | April 16, 2025 |
| Acceptance Date | June 16, 2025 |
| Published in Issue | Year 2025 EARLY ONLINE |
e-ISSN: 2149-3189
The European Research Journal, hosted by Turkish JournalPark ACADEMIC, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
2025