Perceptions of Dentists Towards Artificial Intelligence: Validation of a New Scale

Yıl 2024, Cilt: 27 Sayı: 2, 109 – 117, 30.06.2024

Burak Buldur , Fatih Teke Mehmet Ali Kurt Kaan Sağtaş

https://doi.org/10.7126/cumudj.1411689

Öz

Objectives: To enhance the effectiveness and efficiency of using artificial intelligence (AI) in healthcare, it is crucial to comprehend the perceptions of healthcare professionals and individuals regarding AI. This study aimed to: (i) develop and conduct psychometric analyses of a new measurement tool, the AI Perceptions Scale (AIPS); and (ii) identify and compare sub-dimensions of perceptions of AI and its sub-dimensions, specifically in the dental profession.
Materials and Methods: The study used a cross-sectional and correlational design involving 543 dentists. The data collection tools used were a socio-demographic form, the AIPS, and the Dental Profession Perceptions Scale (DPPS). Construct validity was assessed using exploratory and confirmatory factor analysis. Multivariate analysis of variance was utilized to test the difference between AIPS scores among groups.
Results: The AIPS contained 26 items measured on a 5-point Likert response scale and demonstrated excellent internal and test-retest reliability. Exploratory and confirmatory factor analyses of the AIPS identified six factors that categorized perceptions of AI, including ‘Human’, ‘Security’, ‘Accessibility’, ‘Vocational’, ‘Technology’, and ‘Cost’. The six-factor solution of the AIPS model demonstrated a good fit for the data. AIPS scores varied depending on gender, working place, occupational experience, the need to use AI, and the frequency of AI use in dental practice. The total AIPS score had the strongest correlation with the “human” factor and the weakest correlation with the “accessibility” factor. Statistically significant correlations were observed between the AIPS score and DPPS total, as well as each of its three sub-scales.
Conclusions: This study developed a new scale, the AI Perceptions Scale (AIPS), to evaluate perceptions of AI in healthcare. The perceptions of dentists towards AI were categorized into six distinct factors. The AIPS scale was found to be a reliable and valid measurement tool, indicating that it can be effectively used in future research.

Anahtar Kelimeler

artificial intelligence, attitude of health personnel, dentists, occupational dentistry, perception

Kaynakça

• 1. Kok J N, Boers E J, Kosters W A, et al. Artificial intelligence: definition, trends, techniques, and cases. Artif Intell 2009;1:270-299.
• 2. Eli-Chukwu N C. Applications of artificial intelligence in agriculture: A review. Eng Appl Sci Res 2019;9:4377-4383.
• 3. Poola I. How artificial intelligence in impacting real life everyday. Int J Adv Res Dev 2017;2:96-100.
• 4. Pethani F. Promises and perils of artificial intelligence in dentistry. Aust Dent J 2021;66:124-135.
• 5. Schwendicke F a, Samek W, and Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res 2020;99:769-774.
• 6. Shan T, Tay F, and Gu L. Application of artificial intelligence in dentistry. J Dent Res 2021;100:232-244.
• 7. Kühnisch J, Meyer O, Hesenius M, et al. Caries detection on intraoral images using artificial intelligence. J Dent Res 2022;101:158-165.
• 8. Schwendicke F, Rossi J, Göstemeyer G, et al. Cost-effectiveness of artificial intelligence for proximal caries detection. J Dent Res 2021;100:369-376.
• 9. Mohammad‐Rahimi H, Motamedian S R, Pirayesh Z, et al. Deep learning in periodontology and oral implantology: A scoping review. J Periodontal Res 2022;57:942-951.
• 10. Chen Y-w, Stanley K, and Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 2020;51:248-257.
• 11. Khanagar S B, Al-Ehaideb A, Maganur P C, et al. Developments, application, and performance of artificial intelligence in dentistry–A systematic review. J Dent Sci 2021;16:508-522.
• 12. Joda T, Yeung A, Hung K, et al. Disruptive innovation in dentistry: what it is and what could be next. J Dent Res 2021;100:448-453.
• 13. Nguyen T T, Larrivée N, Lee A, et al. Use of artificial intelligence in dentistry. Current clinical trends and research advances. J Can Dent Assoc 2021;7:17.
• 14. Schwendicke F, Singh T, Lee J-H, et al. Artificial intelligence in dental research: Checklist for authors, reviewers, readers. J Dent 2021;107:103610.
• 15. Ahmed N, Abbasi M S, Zuberi F, et al. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review. Biomed Res Int 2021;2021:e9751564.
• 16. Hsu L-P, Huang Y-K, and Chang Y-C. The implementation of artificial intelligence in dentistry could enhance environmental sustainability. J Dent Sci 2022;17:1081.
• 17. Pauwels R and Del Rey Y C. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey. Dentomaxillofac Radiol 2021;50:20200461.
• 18. Gaube S, Lermer E, and Fischer P. The concept of risk perception in health-related behavior theory and behavior change. Perceived safety 2019;101-118.
• 19. Stai B, Heller N, McSweeney S, et al. Public perceptions of artificial intelligence and robotics in medicine. J Endourol 2020;34:1041-1048.
• 20. Asan O and Choudhury A. Research trends in artificial intelligence applications in human factors health care: mapping review. JMIR Hum Factors 2021;8:e28236.
• 21. Pishgar M, Issa S F, Sietsema M, et al. REDECA: a novel framework to review artificial intelligence and its applications in occupational safety and health. Int J Environ Res Public Health 2021;18:6705.
• 22. Sohn K and Kwon O. Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telemat Inform 2020;47:101324.
• 23. Wolff J, Pauling J, Keck A, et al. The economic impact of artificial intelligence in health care: systematic review. J Med Internet Res 2020;22:e16866.
• 24. Mörch C, Atsu S, Cai W, et al. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res 2021;100:1452-1460.
• 25. Zhang Z, Ning H, Shi F, et al. Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artif Intell Rev 2022;55:1029-1053.
• 26. Li Z, Keel S, and He M. Can artificial intelligence make screening faster, more accurate, and more accessible? Asia Pac J Ophthalmol 2018;7:436-441.
• 27. Miller T. Explanation in artificial intelligence: Insights from the social sciences. Artif Intell 2019;267:1-38.
• 28. Buldur B and Armfield J M. Perceptions of the dental profession: a comparative analysis through scale development. Eur J Oral Sci 2018;126:46-52.
• 29. Hair J F, Anderson R E, Babin B J, et al., Multivariate data analysis: A global perspective (Vol. 7). 2010, Upper Saddle River, NJ: Pearson.
• 30. Brougham D and Haar J. Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. J Manage Organ 2018;24:239-257.
• 31. Laï M-C, Brian M, and Mamzer M-F. Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J Transl Med 2020;18:1-13.
• 32. Chounta I-A, Bardone E, Raudsep A, et al. Exploring teachers’ perceptions of Artificial Intelligence as a tool to support their practice in Estonian K-12 education. Int J Artif Intell Educ 2022;32:725-755.
• 33. Tandon D, Rajawat J, and Banerjee M. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofac Res 2020;10:391-396.
• 34. Tang K-Y, Chang C-Y, and Hwang G-J. Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interact Learn Environ 2021;1-19.
• 35. Müller A, Mertens S M, Göstemeyer G, et al. Barriers and enablers for artificial intelligence in dental diagnostics: a qualitative study. J Clin Med 2021;10:1612.
• 36. Yau H K and Cheng A L F. Gender difference of confidence in using technology for learning. J Technol Res 2012;38:74-79.

DİŞ HEKİMLERİNİN YAPAY ZEKAYA İLİŞKİN ALGILARI: YENİ BİR ÖLÇEĞİN GELİŞTİRİLMESİ VE GEÇERLİLİĞİ

Yıl 2024, Cilt: 27 Sayı: 2, 109 – 117, 30.06.2024

Burak Buldur , Fatih Teke Mehmet Ali Kurt Kaan Sağtaş

https://doi.org/10.7126/cumudj.1411689

Öz

Amaç: Yapay zekanın (yz) sağlık hizmetlerinde kullanımının etkinliğini ve verimliliğini artırmak için, sağlık profesyonellerinin ve bireylerin YZ ile ilgili algılarını anlamak çok önemlidir. Bu çalışmanın amacı: (i) yeni bir ölçüm aracı olan YZ Algılama Ölçeği’nin (YZPS) psikometrik analizlerini geliştirmek ve yürütmek; ve (ii) YZ algılarının alt boyutlarını ve alt boyutlarını, özellikle dişhekimliği mesleğinde belirlemek ve karşılaştırmak.
Gereç ve Yöntem: Çalışmada, 543 diş hekimini içeren kesitsel ve korelasyonel bir tasarım kullanıldı. Kullanılan veri toplama araçları sosyo-demografik bir form, YZPS ve Diş Hekimliği Mesleği Algı Ölçeği’dir (DPPS). Yapı geçerliliği açımlayıcı ve doğrulayıcı faktör analizi kullanılarak değerlendirildi. Gruplar arasında YZPS puanları arasındaki farkı test etmek için çok değişkenli varyans analizi kullanıldı.
Sonuçlar: YZPS, 5 noktalı Likert yanıt ölçeğinde ölçülen 26 madde içeriyordu ve mükemmel dahili ve test-tekrar test güvenilirliği gösterdi. PS’nin açıklayıcı ve doğrulayıcı faktör analizleri, “İnsan”, “Güvenlik”, “Erişilebilirlik”, “Mesleki”, “Teknoloji” ve “Maliyet” dahil olmak üzere yapay zeka algılarını kategorize eden altı faktör belirledi. YZPS modelinin altı faktörlü çözümü, veriler için iyi bir uyum gösterdi. YZPS puanları cinsiyete, çalışma yerine, mesleki deneyime,YZ kullanma ihtiyacına ve dişhekimliği pratiğinde YZ kullanım sıklığına bağlı olarak değişti. Toplam YZPS puanı, “insan” faktörü ile en güçlü korelasyona ve “erişilebilirlik” faktörü ile en zayıf korelasyona sahipti. YZPS puanı ile DPPS toplamı ve üç alt ölçeğinin her biri arasında istatistiksel olarak anlamlı korelasyonlar gözlendi.
Sonuçlar: Bu çalışma, sağlık hizmetlerinde yapay zeka algılarını değerlendirmek için yeni bir ölçek olan YZ Algılama Ölçeği (YZPS) geliştirdi. Diş hekimlerinin yapay zekaya yönelik algıları altı farklı faktöre ayrılmıştır. YZPS ölçeğinin güvenilir ve geçerli bir ölçme aracı olması, gelecekte yapılacak araştırmalarda etkin bir şekilde kullanılabileceğini göstermektedir.

Anahtar Kelimeler

Yapay zeka, sağlık personelinin tutumu, diş hekimleri, işyeri diş hekimliği, algı

Kaynakça

• 1. Kok J N, Boers E J, Kosters W A, et al. Artificial intelligence: definition, trends, techniques, and cases. Artif Intell 2009;1:270-299.
• 2. Eli-Chukwu N C. Applications of artificial intelligence in agriculture: A review. Eng Appl Sci Res 2019;9:4377-4383.
• 3. Poola I. How artificial intelligence in impacting real life everyday. Int J Adv Res Dev 2017;2:96-100.
• 4. Pethani F. Promises and perils of artificial intelligence in dentistry. Aust Dent J 2021;66:124-135.
• 5. Schwendicke F a, Samek W, and Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res 2020;99:769-774.
• 6. Shan T, Tay F, and Gu L. Application of artificial intelligence in dentistry. J Dent Res 2021;100:232-244.
• 7. Kühnisch J, Meyer O, Hesenius M, et al. Caries detection on intraoral images using artificial intelligence. J Dent Res 2022;101:158-165.
• 8. Schwendicke F, Rossi J, Göstemeyer G, et al. Cost-effectiveness of artificial intelligence for proximal caries detection. J Dent Res 2021;100:369-376.
• 9. Mohammad‐Rahimi H, Motamedian S R, Pirayesh Z, et al. Deep learning in periodontology and oral implantology: A scoping review. J Periodontal Res 2022;57:942-951.
• 10. Chen Y-w, Stanley K, and Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 2020;51:248-257.
• 11. Khanagar S B, Al-Ehaideb A, Maganur P C, et al. Developments, application, and performance of artificial intelligence in dentistry–A systematic review. J Dent Sci 2021;16:508-522.
• 12. Joda T, Yeung A, Hung K, et al. Disruptive innovation in dentistry: what it is and what could be next. J Dent Res 2021;100:448-453.
• 13. Nguyen T T, Larrivée N, Lee A, et al. Use of artificial intelligence in dentistry. Current clinical trends and research advances. J Can Dent Assoc 2021;7:17.
• 14. Schwendicke F, Singh T, Lee J-H, et al. Artificial intelligence in dental research: Checklist for authors, reviewers, readers. J Dent 2021;107:103610.
• 15. Ahmed N, Abbasi M S, Zuberi F, et al. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review. Biomed Res Int 2021;2021:e9751564.
• 16. Hsu L-P, Huang Y-K, and Chang Y-C. The implementation of artificial intelligence in dentistry could enhance environmental sustainability. J Dent Sci 2022;17:1081.
• 17. Pauwels R and Del Rey Y C. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey. Dentomaxillofac Radiol 2021;50:20200461.
• 18. Gaube S, Lermer E, and Fischer P. The concept of risk perception in health-related behavior theory and behavior change. Perceived safety 2019;101-118.
• 19. Stai B, Heller N, McSweeney S, et al. Public perceptions of artificial intelligence and robotics in medicine. J Endourol 2020;34:1041-1048.
• 20. Asan O and Choudhury A. Research trends in artificial intelligence applications in human factors health care: mapping review. JMIR Hum Factors 2021;8:e28236.
• 21. Pishgar M, Issa S F, Sietsema M, et al. REDECA: a novel framework to review artificial intelligence and its applications in occupational safety and health. Int J Environ Res Public Health 2021;18:6705.
• 22. Sohn K and Kwon O. Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telemat Inform 2020;47:101324.
• 23. Wolff J, Pauling J, Keck A, et al. The economic impact of artificial intelligence in health care: systematic review. J Med Internet Res 2020;22:e16866.
• 24. Mörch C, Atsu S, Cai W, et al. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res 2021;100:1452-1460.
• 25. Zhang Z, Ning H, Shi F, et al. Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artif Intell Rev 2022;55:1029-1053.
• 26. Li Z, Keel S, and He M. Can artificial intelligence make screening faster, more accurate, and more accessible? Asia Pac J Ophthalmol 2018;7:436-441.
• 27. Miller T. Explanation in artificial intelligence: Insights from the social sciences. Artif Intell 2019;267:1-38.
• 28. Buldur B and Armfield J M. Perceptions of the dental profession: a comparative analysis through scale development. Eur J Oral Sci 2018;126:46-52.
• 29. Hair J F, Anderson R E, Babin B J, et al., Multivariate data analysis: A global perspective (Vol. 7). 2010, Upper Saddle River, NJ: Pearson.
• 30. Brougham D and Haar J. Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. J Manage Organ 2018;24:239-257.
• 31. Laï M-C, Brian M, and Mamzer M-F. Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J Transl Med 2020;18:1-13.
• 32. Chounta I-A, Bardone E, Raudsep A, et al. Exploring teachers’ perceptions of Artificial Intelligence as a tool to support their practice in Estonian K-12 education. Int J Artif Intell Educ 2022;32:725-755.
• 33. Tandon D, Rajawat J, and Banerjee M. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofac Res 2020;10:391-396.
• 34. Tang K-Y, Chang C-Y, and Hwang G-J. Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interact Learn Environ 2021;1-19.
• 35. Müller A, Mertens S M, Göstemeyer G, et al. Barriers and enablers for artificial intelligence in dental diagnostics: a qualitative study. J Clin Med 2021;10:1612.
• 36. Yau H K and Cheng A L F. Gender difference of confidence in using technology for learning. J Technol Res 2012;38:74-79.

Toplam 36 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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