The Role of Artificial Intelligence in Managing Voice Disorders

Öz

Artificial intelligence has evolved beyond being a term coined in the 20th century and continues to be a field with great potential in many areas today. Developments in artificial intelligence are rapidly spreading in many areas, such as healthcare. New artificial intelligence technologies continue to develop and spread in the fields of language and speech therapy. One such field is voice disorders. Although there are many assessment and therapy tools used in the management of voice disorders today, the development and potential of artificial intelligence necessitates research in this area. This review highlights the development of artificial intelligence in the management of voice disorders and possible ethical issues. Although the literature has found that machine learning and deep learning algorithms achieve high accuracy, sensitivity, and specificity rates in evaluating voice disorders and provide clinicians with objective data during the evaluation process, no evidence has been found regarding their benefits in the therapy process. AI-based wearable technologies can assist clinicians in the assessment and therapy processes. Despite the potential of artificial intelligence, certain ethical issues cannot be ignored. There are also threats to clinical decision-making and patient safety.

Anahtar Kelimeler

• Artificial İntelligence
• Voice Disorders
• Privacy

Ses Bozukluğunun Yönetiminde Yapay Zekanın Rolü

Öz

ÖZ Yapay zekâ, 20. yüzyılda ortaya atılan bir terim olmaktan çıkıp günümüzde birçok alanda büyük potansiyeller barındıran bir alan olmaya devam etmektedir. Yapay zekânın sağlık hizmetleri gibi birçok alanda gelişmeleri hızla yaygınlaşmaktadır. Yeni yapay zekâ teknolojileri dil ve konuşma terapisi alanlarında da gelişimini sürdürerek yaygınlaşmaya devam etmektedir. Bu alanlardan biri de ses bozuklukları alanıdır. Günümüzde ses bozukluklarının yönetiminde kullanılan birçok değerlendirme ve terapi aracı bulunsa da yapay zekanın gelişimi ve potansiyeli bu alandaki araştırmaları zorunlu kılmaktadır. Bu derleme, ses bozukluklarının yönetiminde yapay zekanın gelişimini ve olası etik problemleri ortaya koymaktadır. Literatürde makine öğrenimi ve derin öğrenme algoritmalarının ses bozukluklarını değerlendirmede yüksek doğruluk, duyarlılık ve özgüllük oranları yakalandığı ve klinisyenlere değerlendirme sürecinde objektif veriler sağladığı bulgusuna ulaşılsa da terapi sürecindeki faydasına yönelik bir bulguya rastlanmamıştır. Yapay zekâ temelli giyilebilir teknolojilerin klinisyenlere değerlendirme ve terapi süreçlerinde yardımcı olabilir. Yapay zekanın bu potansiyeline rağmen birtakım etik sorunların göz ardı edilemeyeceği, klinik karar alma ve hasta güvenliğine yönelik tehditlerin mevcut olduğu belirtilmiştir.

Anahtar Kelimeler

• Yapay Zeka
• Ses Bozuklukları
• Gizlilik

Kaynakça

1. 1. Öztemel E. Yapay Zekâ ve İnsanlığın Geleceği. Bilişim Teknolojileri ve İletişim: Birey ve Toplum Güvenliği. 2020;75–90. doi:10.53478/tuba.2020.011
2. 2. Abbass H. Editorial: What is Artificial Intelligence?. IEEE Trans Artif Intell. 2021 Apr;2(2):94-5. doi:10.1109/TAI.2021.3096243
3. 3. Sheikh H, Prins C, Schrijvers E. Mission AI: The New System Technology. Cham, Switzerland: Springer; 2023.
4. 4. Wang P. On Defining Artificial Intelligence. Journal of Artificial General Intelligence. 2019 Jan 1;10(2):1-37.
5. 5. Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach. 3rd ed. Harlow (UK): Pearson Education Limited; 2016.
6. 6. Bhosale SS, Salunkhe AG, Sutar SS. Artificial intelligence and its application in different areas. Int. j. eng. innov. technol. 2020;7(1):35-39.
7. 7. Türker Şener L, Bozkaya DN, Kıtır T. COVID-19 Sürecindeki Yapay Zeka, Dijital Sağlık Tanı ve Tedavisindeki Gelişmeler. JAIHS. 2022;2(1):13-20. doi:10.52309/jaihs.v2i1.37
8. 8. Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A Review of the Role of Artificial Intelligence in Healthcare. JPM. 2023 Jun 5;13(6):951. doi: 10.3390/jpm13060951

9. 9. Krstić, L., Aleksić, V. and Krstić, M. (2022) ‘Artificial Intelligence in education: A Review’, Proceedings TIE 2022, pp. 223–228. doi:10.46793/tie22.223k.
10. 10. Bharadiya J. Artificial Intelligence in Transportation Systems A Critical Review. AJCE. 2023 Jun 3;6(1):34-45. doi:10.47672/ajce.1487
11. 11. ASHA Scope Of Practıce In Speech Language Pathology [Internet]. [cited 12 May 2025]. Available from: https://www.asha.org/siteassets/publications/sp2016-00343.pdf
12. 12. DKTD Dil ve Konuşma Terapisti Kimdir [Internet]. [cited 13 May 2025]. Available from: https://www.dktd.org/tr/files/download/p1e8tpiusl15n41h7pq2n1ib35u34.pdf
13. 13. Bhardwaj A, Sharma M, Kumar S, Sharma S, Sharma PC. Transforming pediatric speech and language disorder diagnosis and therapy: The evolving role of Artificial Intelligence. Health Sciences Review. 2024 Sept; 12:100188. doi: 10.1016/j.hsr.2024.100188
14. 14. Suh H, Dangol A, Meadan H, Miller CA, Kientz JA. Opportunities and challenges for AI-based support for speech-language pathologists. Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work. 2024 Jun 25;1–14. doi:10.1145/3663384.3663387
15. 15. 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. 2023 Mar 31;38(2):305–36. doi:10.1080/02687038.2023.2189513
16. 16. Brahmi Z, Mahyoob M, Al-Sarem M, Algaraady J, Bousselmi K, Alblwi A. Exploring the role of machine learning in diagnosing and treating speech disorders: A system atic literature review. Psychology Research and Behavior Management. 2024 May;Volume 17:2205–32. doi:10.2147/prbm.s460283
17. 17. Jeong C-W, Lee C-S, Lim D-W, Noh S-H, Moon H-K, Park C, et al. The development of an artificial intelligence video analysis-based web application to diagnose oropharyngeal dysphagia: A pilot study. Brain Sciences. 2024 May 27;14(6):546. doi:10.3390/brainsci14060546
18. 18. Alnashwan R, Alhakbani N, Al-Nafjan A, Almudhi A, Al-Nuwaiser W. Computational intelligence-based stuttering detection: A systematic review. Diagnostics. 2023 Nov 27;13(23):3537. doi:10.3390/diagnostics13233537
19. 19. Kim H-B, 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. Scientific Reports. 2024 Apr 23;14(1). doi:10.1038/s41598-024-58817-x
20. 20. Sundas A, Badotra S, Rani S, Gyaang R. Evaluation of autism spectrum disorder based on the healthcare by using Artificial Intelligence Strategies. Journal of Sensors. 2023 Jan;2023(1). doi:10.1155/2023/5382375
21. 21. Ferrand CT. Voice disorders: scope of theory and practice. New York: Pearson; 2019.
22. 22. Aronson AE, Bless DM. Clinical voice disorders. New York: Thieme; 2014.
23. 23. Umeno H, Hyodo M, Haji T, Hara H, Imaizumi M, Ishige M, et al. A summary of the clinical practice guideline for the diagnosis and management of Voice Disorders, 2018 in Japan. Auris Nasus Larynx. 2020 Feb;47(1):7–17. doi: 10.1016/j.anl.2019.09.004
24. 24. Stemple JC, Roy N, Klaben B. Clinical Voice Pathology: Theory and Management. San Diego, CA: Plural Publishing, Inc; 2020.
25. 25. Van Stan JH, Roy N, Awan S, Stemple J, Hillman RE. A taxonomy of voice therapy. American Journal of Speech-Language Pathology. 2015 May;24(2):101–25. doi:10.1044/2015_ajslp-14-0030
26. 26. Mailänder E, Mühre L, Barsties B. Lax Vox as a voice training program for teachers: A pilot study. Journal of Voice. 2017 Mar;31(2). doi: 10.1016/j.jvoice.2016.04.011
27. 27. Jafari N, Salehi A, Izadi F, Talebian Moghadam S, Ebadi A, Dabirmoghadam P, et al. Vocal function exercises for muscle tension dysphonia: Auditory-perceptual evaluation and self-assessment rating. Journal of Voice. 2017 Jul;31(4). doi:10.1016/j.jvoice.2016.10.009
28. 28. Idrisoglu A, Dallora AL, Anderberg P, Berglund JS. Applied machine learning techniques to diagnose voice-affecting conditions and disorders: Systematic literature review. Journal of Medical Internet Research. 2023 Jul 19;25. doi:10.2196/46105
29. 29. Bolat ES, Umurhan E, Türe N. Ses Hastalıklarında Yapay Zekâ: Bibliyometrik Analiz. 44. Türk Ulusal Kulak Burun Boğaz Ve Baş Boyun Cerrahisi Kongresi; 2022. Antalya, Turkey. p. 3–4.
30. 30. Suvvari TK. The role of artificial intelligence in diagnosis and management of laryngeal disorders. Ear, Nose & Throat Journal. 2023 May 17;014556132311750. doi:10.1177/01455613231175053
31. 31. Liu GS, Jovanovic N, Sung CK, Doyle PC. A scoping review of artificial intelligence detection of voice pathology: Challenges and opportunities. Otolaryngology–Head and Neck Surgery. 2024 May 13;171(3):658–66. doi:10.1002/ohn.809
32. 32. Al-Dhief FT, Latiff NM, Malik NN, Salim NS, Baki MM, Albadr MA, et al. A survey of voice pathology surveillance systems based on internet of things and machine learning algorithms. IEEE Access. 2020;8:64514–33. doi:10.1109/access.2020.2984925
33. 33. Buongiorno R, Caudai C, Colantonio S, Germanese D. Introduction to machine learning in medicine. Imaging Informatics for Healthcare Professionals. 2023;39–68. doi:10.1007/978-3-031-25928-9_3
34. 34. Van Stan JH, Mehta DD, Hillman RE. Recent innovations in voice assessment expected to impact the clinical management of Voice Disorders. Perspectives of the ASHA Special Interest Groups. 2017 Jan;2(3):4–13. doi: 10.1044/persp2.sig3.4
35. 35. Oladipupo T. Types of machine learning algorithms. New Advances in Machine Learning. 2010 Feb 1; doi:10.5772/9385
36. 36. Sah S. Machine learning: A review of learning types. 2020 Jul 11; doi:10.20944/preprints202007.0230.v1
37. 37. Breiman L. Random forests. Machine Learning. 2001 Oct;45(1):5–32. doi:10.1023/a:1010933404324
38. 38. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. 2017 Oct 19; doi:10.1201/9781315139470
39. 39. Cortes C, Vapnik V. Support-Vector Networks. Machine Learning. 1995 Sept;20(3):273–97. doi:10.1007/bf00994018
40. 40. 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;7:124048–54. doi:10.1109/access.2019.2938265
41. 41. Kim H, Jeon J, Han YJ, Joo Y, Lee J, Lee S, et al. Convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy. Journal of Clinical Medicine. 2020 Oct 25;9(11):3415. doi:10.3390/jcm9113415
42. 42. 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. Journal of Medical Internet Research. 2022 Oct 14;24(10). doi:10.2196/38472
43. 43. Peng X, Xu H, Liu J, Wang J, He C. Voice disorder classification using convolutional neural network based on Deep Transfer Learning. Scientific Reports. 2023 May 4;13(1). doi:10.1038/s41598-023-34461-9
44. 44. Di Cesare MG, Perpetuini D, Cardone D, Merla A. Assessment of voice disorders using machine learning and vocal analysis of voice samples recorded through smartphones. BioMedInformatics. 2024 Feb 19;4(1):549–65. doi:10.3390/biomedinformatics4010031
45. 45. Godino-Llorente JI, Gómez-Vilda P, Sáenz-Lechón N, Blanco-Velasco M, Cruz-Roldán F, Ferrer-Ballester MA. Support vector machines applied to the detection of voice disorders. Lecture Notes in Computer Science. 2006;219–30. doi:10.1007/11613107_19
46. 46. Verde L, De Pietro G, Sannino G. Voice disorder identification by using machine learning techniques. IEEE Access. 2018;6:16246–55. doi:10.1109/access.2018.2816338
47. 47. Lee J-Y, Park JH, Lee J-N, Jung AR. Personal and clinical predictors of voice therapy outcomes: A machine learning analysis using the voice handicap index. Applied Sciences. 2024 Nov 11;14(22):10376. doi:10.3390/app142210376
48. 48. Ur Rehman M, Shafique A, Azhar Q-U-A, Jamal SS, Gheraibia Y, Usman AB. Voice disorder detection using machine learning algorithms: An application in speech and language pathology. Engineering Applications of Artificial Intelligence. 2024 Jul; 133:108047. doi: 10.1016/j.engappai.2024.108047
49. 49. Bowden M. A review of textual and voice processing algorithms in the field of Natural Language Processing. Journal of Computing and Natural Science. 2023 Oct 5;194–203. doi:10.53759/181x/jcns202303018
50. 50. Trivizakis E, Marias K. Deep Learning Fundamentals. Imaging Informatics for Healthcare Professionals. 2023;101–31. doi:10.1007/978-3-031-25928-9_6
51. 51. Akın E, Şahin ME. Derin öğrenme ve Yapay Sinir Ağı modelleri üzerine Bir inceleme [Internet]. TMMOB Elektrik Mühendisleri Odası; 2024 [alıntı tarihi: 23 Mayıs 2025]. Şu adresten ulaşılabilir: https://dergipark.org.tr/tr/pub/emobd/issue/83029/1338066
52. 52. Aggarwal CC. Neural networks and deep learning: A textbook. Cham: Springer; 2018.
53. 53. Kim KG. Book review: Deep learning. Healthcare Informatics Research. 2016;22(4):351. doi:10.4258/hir.2016.22.4.351
54. 54. Fang S-H, Tsao Y, Hsiao M-J, Chen J-Y, Lai Y-H, Lin F-C, et al. Detection of pathological voice using cepstrum vectors: A deep learning approach. Journal of Voice. 2019 Sept;33(5):634–41. doi: 10.1016/j.jvoice.2018.02.003
55. 55. Lilhore UK, Dalal S, Faujdar N, Margala M, Chakrabarti P, Chakrabarti T, et al. Retracted article: Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of parkinson’s disease. Scientific Reports. 2023 Sept 5;13(1). doi:10.1038/s41598-023-41314-y
56. 56. Fantini M, Ciravegna G, Koudounas A, Cerquitelli T, Baralis E, Succo G, et al. The rapidly evolving scenario of Acoustic Voice Analysis in otolaryngology. Cureus. 2024 Nov 11; doi:10.7759/cureus.73491
57. 57. Mehta DD, Van Stan JH, Zañartu M, Ghassemi M, Guttag JV, Espinoza VM, et al. Using ambulatory voice monitoring to investigate common voice disorders: Research update. Frontiers in Bioengineering and Biotechnology. 2015 Oct 16;3. doi:10.3389/fbioe.2015.00155
58. 58. Mahnan A, Konczak J, Faraji SA. Wearable non-invasive neuromodulation device for the symptomatic treatment of the voice disorder Spasmodic Dysphonia. 2019 Design of Medical Devices Conference. 2019 Apr 15; doi:10.1115/dmd2019-3219
59. 59. Dubey H, Goldberg JC, Abtahi M, Mahler L, Mankodiya K. Echowear. Proceedings of the conference on Wireless Health. 2015 Oct 14;1–8. doi:10.1145/2811780.2811957
60. 60. Llorente M, Podhorski A, Fernandez S. Wearable Voice Dosimetry System. Applied Sciences. 2024 Jul 3;14(13):5806. doi:10.3390/app14135806
61. 61. Karimian G, Petelos E, Evers SM. The ethical issues of the application of artificial intelligence in Healthcare: A systematic scoping review. AI and Ethics. 2022 Mar 28;2(4):539–51. doi:10.1007/s43681-021-00131-7
62. 62. WHO Ethıcs And Governance Of Artıfıcıal Intellıgence For Health [Internet]. [cited 25 May 2025]. Available from: https://iris.who.int/bitstream/handle/10665/341996/9789240029200-eng.pdf
63. 63. The Swedish National Council On Medical Ethics [Internet]. [cited 25 May 2025]. Available from: https://smer.se/wp-content/uploads/2020/06/smer-2020-2-in-brief-artificial-intelligence-in-healthcare.pdf
64. 64. Rosemann A, Zhang X. Exploring the social, ethical, legal, and responsibility dimensions of Artificial Intelligence for Health – a new column in Intelligent Medicine. Intelligent Medicine. 2022 May;2(2):103–9. doi: 10.1016/j.imed.2021.12.002
65. 65. Matheny ME, Whicher D, Thadaney Israni S. Artificial Intelligence in health care. JAMA. 2020 Feb 11;323(6):509. doi:10.1001/jama.2019.21579
66. 66. Ford MR. Rise of the robots: Technology and the threat of jobless future. New York: Basic Books; 2016.
67. 67. Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial Intelligence for Good Health: A scoping review of the ethics literature. BMC Medical Ethics. 2021 Feb 15;22(1). doi:10.1186/s12910-021-00577-8