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Diş Hekimliği Öğrencilerinin Yapay Zeka ile İlgili Bilgi ve Farkındalıkları: Trakya Üniversitesinde Yapılmış Bir Kesitsel Çalışma

Yıl 2026, Cilt: 28 Sayı: 1 , 55 - 63 , 27.04.2026
https://doi.org/10.24938/kutfd.1787152
https://izlik.org/JA46RY56ZJ

Öz

Amaç: Bu çalışmanın amacı, Trakya Üniversitesi Diş Hekimliği Fakültesi öğrencilerinin diş hekimliğinde yapay zekâ (YZ) kullanımına ilişkin bilgi düzeyleri, farkındalıkları ve tutumlarını değerlendirmektir.
Gereç ve Yöntemler: Kesitsel nitelikteki bu anket çalışması, 2., 3., 4. ve 5. sınıfta öğrenim gören diş hekimliği öğrencileri arasında yürütülmüştür. Veriler, Ekim 2024 ile Şubat 2025 tarihleri arasında, 20 sorudan oluşan çevrimiçi bir anket formu aracılığıyla toplanmıştır. Değişkenler arasındaki ilişkiler Fisher’in Kesin Testi ve Bonferroni düzeltmeli Z-testleri ile analiz edilmiştir (p<0,05).
Bulgular: Çalışmaya toplam 460 öğrenci katılmıştır. Katılımcıların %61,5’i diş hekimliğinde yapay zekâ hakkında temel bilgiye sahip olduğunu, %37,4’ü herhangi bir bilgiye sahip olmadığını, yalnızca %1,1’i ileri düzey bilgiye sahip olduğunu bildirmiştir. Bilgi düzeyi ile YZ’nin diş hekimliği açısından önemine yönelik inanç arasında anlamlı bir ilişki saptanmıştır (p<0,05). Yapay zekâya duyulan güven, gelecekte klinik uygulamalarda kullanım isteğiyle güçlü bir şekilde ilişkili bulunmuştur (p<0,001). Öğrencilerin büyük çoğunluğu yapay zekâyı teşhis hatalarını azaltan ve bakım kalitesini artıran destekleyici bir araç olarak görürken, klinisyen yargısının yerine geçebilecek bir sistem olarak değerlendirmemiştir. Eğitim bağlamında ise katılımcıların %77,8’i yapay zekânın öğrenme sürecini hızlandırabileceğini belirtmiş ve çoğunluğu müfredata entegrasyonunu desteklemiştir.
Sonuç: Diş hekimliği öğrencilerinin yapay zekâ konusundaki bilgi düzeyi sınırlı olmakla birlikte, genel tutumlarının olumlu olduğu belirlenmiştir. yapay zekâya duyulan güven, klinik uygulamalarda benimsenme isteğini etkileyen temel unsur olarak öne çıkmaktadır. Yapay zekânın diş hekimliği müfredatına entegre edilmesi, geleceğin diş hekimlerinin bu teknolojileri etkin ve etik biçimde kullanabilmeleri açısından önemli katkılar sağlayabilir.

Kaynakça

  • Gignac GE, Szodorai ET. Defining intelligence: Bridging the gap between human and artificial perspectives. Intelligence. 2024;104:101832.
  • Kufel J, Bargieł-Łączek K, Kocot S, et al. What is machine learning, artificial neural networks and deep learning? Diagnostics. 2023;13(15):2582.
  • Kim HE, Cosa-Linan A, Santhanam N, et al. Transfer learning for medical image classification: A literature review. BMC Med Imaging. 2022;22(1):69.
  • Orhan K, Jagtap R. Introduction to artificial intelligence. Springer Int Publ. 2023:1-7.
  • Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020;99(7):769-774.
  • Setzer FC, Li J, Khan AA. The use of artificial intelligence in endodontics. J Dent Res. 2024;103(9):853-862.
  • Patwardhan N, Marrone S, Sansone C. Transformers in the real world: A survey on NLP applications. Information. 2023;14(4):242.
  • Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877-1901.
  • Aminoshariae A, Nosrat A, Nagendrababu V, et al. Artificial intelligence in endodontic education. J Endod. 2024;50(5):562-578.
  • Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020;99(7):769-774.
  • Dashti M, Londono J, Ghasemi S, et al. Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: A comprehensive umbrella review. PeerJ Comput Sci. 2024;10:e2371.
  • Mertens S, Krois J, Cantu AG, et al. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021;115:103849.
  • Orhan K, Bayrakdar IS, Ezhov M, et al. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53(5):680-689.
  • Subramanian AK, Chen Y, Almalki A, et al. Cephalometric analysis in orthodontics using artificial intelligence: A comprehensive review. Biomed Res Int. 2022;2022(1):1880113.
  • Kurt Bayrakdar S, Orhan K, Bayrakdar IS, et al. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging. 2021;21(1):168.
  • Krois J, Ekert T, Meinhold L, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;9(1):8493.
  • Hoss P, Meyer O, Wölfle UC, et al. Detection of periodontal bone loss on periapical radiographs: A diagnostic study using different convolutional neural networks. J Clin Med. 2023;12(22):7189.
  • Wang Y, Xia W, Yan Z, et al. Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning. Med Image Anal. 2023;85:102750.
  • Sherwood AA, Sherwood AI, Setzer FC, et al. A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography. J Endod. 2021;47(12):1907-1916.
  • Duman SB, Çelik Özen D, Bayrakdar IS, et al. Second mesiobuccal canal segmentation with YOLOv5 architecture using cone-beam computed tomography images. Odontology. 2024;112(2):552-561.
  • Saghiri MA, Garcia-Godoy F, Gutmann JL, et al. The reliability of artificial neural network in locating minor apical foramen: A cadaver study. J Endod. 2012;38(8):1130-1134.
  • Balasundaram A, Shah P, Hoen MM, et al. Comparison of cone-beam computed tomography and periapical radiography in predicting treatment decision for periapical lesions: A clinical study. Int J Dent. 2012;2012:920815.
  • Sadr S, Mohammad-Rahimi H, Motamedian SR, et al. Deep learning for detection of periapical radiolucent lesions: A systematic review and meta-analysis of diagnostic test accuracy. J Endod. 2023;49(3):248-261.
  • Schwendicke F, Chaurasia A, Wiegand T, et al. Artificial intelligence for oral and dental healthcare: Core education curriculum. J Dent. 2023;128:104363.
  • Aminoshariae A, Nosrat A, Nagendrababu V, et al. Artificial intelligence in endodontic education. J Endod. 2024;50(5):562-578.
  • Pauwels R, Del Rey YC. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: A multicenter survey. Dentomaxillofac Radiol. 2021;50(5):20200461.
  • Qutieshat A, Al Rusheidi A, Al Ghammari S, et al. Comparative analysis of diagnostic accuracy in endodontic assessments: Dental students vs. artificial intelligence. Diagnosis. 2024;11(3):259-265.
  • Keser G, Namdar Pekiner FM. Attitudes, perceptions and knowledge regarding the future of artificial intelligence in oral radiology among a group of dental students in Turkey: A survey. Clin Exp Health Sci. 2021;11(4):637-641.
  • Yılmaz C, Altınok Uygun L. Artificial intelligence knowledge, attitudes and application perspectives of undergraduate and specialty students of faculty of dentistry in Turkey: An online survey research. BMC Med Educ. 2024;24:534.
  • Elchaghaby M, Wahby R. Knowledge, attitudes, and perceptions of a group of Egyptian dental students toward artificial intelligence: A cross-sectional study. BMC Oral Health. 2025;25(1):11.
  • Jeong H, Han SS, Kim KE, et al. Korean dental hygiene students’ perceptions and attitudes toward artificial intelligence: An online survey. J Dent Educ. 2023;87(6):804-812.
  • Cave S, Dihal K. Hopes and fears for intelligent machines in fiction and reality. Nat Mach Intell. 2019;1(2):74-78.
  • Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98.
  • Lal A, Nooruddin A, Umer F. Concerns regarding deployment of AI-based applications in dentistry: A review. BDJ Open. 2025;11(1):31.
  • Suárez A, Díaz-Flores García V, Algar J, et al. Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers. Int Endod J. 2024;57(1):108-113.
  • Jeong H, Park IS, Choi Y, et al. Korean dental hygiene students’ perceptions and attitudes toward artificial intelligence: An online survey. J Dent Educ. 2023;87(6):804-812.
  • Yüzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2020;84(5):556-562.
  • Elchaghaby M, Wahby R. Knowledge, attitudes, and perceptions of a group of Egyptian dental students toward artificial intelligence: A cross-sectional study. BMC Oral Health. 2025;25(1):11.
  • Qutieshat A, Al Rusheidi A, Al Ghammari S, et al. Comparative analysis of diagnostic accuracy in endodontic assessments: Dental students vs. artificial intelligence. Diagnosis. 2024;11(3):259-265.
  • Gilson A, Safranek CW, Huang T, et al. How does ChatGPT perform on the United States Medical Licensing Examination (USMLE)? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9(1):e45312.

DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY

Yıl 2026, Cilt: 28 Sayı: 1 , 55 - 63 , 27.04.2026
https://doi.org/10.24938/kutfd.1787152
https://izlik.org/JA46RY56ZJ

Öz

Objective: To evaluate the knowledge, awareness, and attitudes of dental students at Trakya University regarding the use of artificial intelligence (AI) in dentistry.
Material and Methods: A cross-sectional survey was conducted among 2nd-, 3rd-, 4th-, and 5th-year dental students. Data were collected from October 2024 to February 2025 using a 20-item online questionnaire. Associations between variables were analyzed using Fisher’s Exact Test and Bonferroni-corrected Z-tests (p<0.05).
Results: A total of 460 students participated. 61.5% reported basic knowledge of artificial intelligence in dentistry, 37.4% had no knowledge, and only 1.1% reported advanced knowledge. Knowledge level was significantly associated with students’ belief in the importance of artificial intelligence in dentistry (p<0.05). Trust was in artificial intelligence strongly correlated with willingness to use it in future clinical practice (p<0.001). Most students viewed artificial intelligence as a supportive tool for reducing diagnostic errors and improve care, but not as a replacement for clinician judgment. In education, 77.8% agreed that artificial intelligence could accelerate learning, and the majority supported its integration into curricula.
Conclusion: Dental students demonstrated limited knowledge but generally positive attitudes toward AI. Trust was the primary factor influencing willingness to adopt artificial intelligence in clinical practice. Incorporating AI into dental curricula may help prepare future dentists to use artificial intelligence technologies effectively and ethically.

Kaynakça

  • Gignac GE, Szodorai ET. Defining intelligence: Bridging the gap between human and artificial perspectives. Intelligence. 2024;104:101832.
  • Kufel J, Bargieł-Łączek K, Kocot S, et al. What is machine learning, artificial neural networks and deep learning? Diagnostics. 2023;13(15):2582.
  • Kim HE, Cosa-Linan A, Santhanam N, et al. Transfer learning for medical image classification: A literature review. BMC Med Imaging. 2022;22(1):69.
  • Orhan K, Jagtap R. Introduction to artificial intelligence. Springer Int Publ. 2023:1-7.
  • Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020;99(7):769-774.
  • Setzer FC, Li J, Khan AA. The use of artificial intelligence in endodontics. J Dent Res. 2024;103(9):853-862.
  • Patwardhan N, Marrone S, Sansone C. Transformers in the real world: A survey on NLP applications. Information. 2023;14(4):242.
  • Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877-1901.
  • Aminoshariae A, Nosrat A, Nagendrababu V, et al. Artificial intelligence in endodontic education. J Endod. 2024;50(5):562-578.
  • Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020;99(7):769-774.
  • Dashti M, Londono J, Ghasemi S, et al. Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: A comprehensive umbrella review. PeerJ Comput Sci. 2024;10:e2371.
  • Mertens S, Krois J, Cantu AG, et al. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021;115:103849.
  • Orhan K, Bayrakdar IS, Ezhov M, et al. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020;53(5):680-689.
  • Subramanian AK, Chen Y, Almalki A, et al. Cephalometric analysis in orthodontics using artificial intelligence: A comprehensive review. Biomed Res Int. 2022;2022(1):1880113.
  • Kurt Bayrakdar S, Orhan K, Bayrakdar IS, et al. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging. 2021;21(1):168.
  • Krois J, Ekert T, Meinhold L, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;9(1):8493.
  • Hoss P, Meyer O, Wölfle UC, et al. Detection of periodontal bone loss on periapical radiographs: A diagnostic study using different convolutional neural networks. J Clin Med. 2023;12(22):7189.
  • Wang Y, Xia W, Yan Z, et al. Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning. Med Image Anal. 2023;85:102750.
  • Sherwood AA, Sherwood AI, Setzer FC, et al. A deep learning approach to segment and classify C-shaped canal morphologies in mandibular second molars using cone-beam computed tomography. J Endod. 2021;47(12):1907-1916.
  • Duman SB, Çelik Özen D, Bayrakdar IS, et al. Second mesiobuccal canal segmentation with YOLOv5 architecture using cone-beam computed tomography images. Odontology. 2024;112(2):552-561.
  • Saghiri MA, Garcia-Godoy F, Gutmann JL, et al. The reliability of artificial neural network in locating minor apical foramen: A cadaver study. J Endod. 2012;38(8):1130-1134.
  • Balasundaram A, Shah P, Hoen MM, et al. Comparison of cone-beam computed tomography and periapical radiography in predicting treatment decision for periapical lesions: A clinical study. Int J Dent. 2012;2012:920815.
  • Sadr S, Mohammad-Rahimi H, Motamedian SR, et al. Deep learning for detection of periapical radiolucent lesions: A systematic review and meta-analysis of diagnostic test accuracy. J Endod. 2023;49(3):248-261.
  • Schwendicke F, Chaurasia A, Wiegand T, et al. Artificial intelligence for oral and dental healthcare: Core education curriculum. J Dent. 2023;128:104363.
  • Aminoshariae A, Nosrat A, Nagendrababu V, et al. Artificial intelligence in endodontic education. J Endod. 2024;50(5):562-578.
  • Pauwels R, Del Rey YC. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: A multicenter survey. Dentomaxillofac Radiol. 2021;50(5):20200461.
  • Qutieshat A, Al Rusheidi A, Al Ghammari S, et al. Comparative analysis of diagnostic accuracy in endodontic assessments: Dental students vs. artificial intelligence. Diagnosis. 2024;11(3):259-265.
  • Keser G, Namdar Pekiner FM. Attitudes, perceptions and knowledge regarding the future of artificial intelligence in oral radiology among a group of dental students in Turkey: A survey. Clin Exp Health Sci. 2021;11(4):637-641.
  • Yılmaz C, Altınok Uygun L. Artificial intelligence knowledge, attitudes and application perspectives of undergraduate and specialty students of faculty of dentistry in Turkey: An online survey research. BMC Med Educ. 2024;24:534.
  • Elchaghaby M, Wahby R. Knowledge, attitudes, and perceptions of a group of Egyptian dental students toward artificial intelligence: A cross-sectional study. BMC Oral Health. 2025;25(1):11.
  • Jeong H, Han SS, Kim KE, et al. Korean dental hygiene students’ perceptions and attitudes toward artificial intelligence: An online survey. J Dent Educ. 2023;87(6):804-812.
  • Cave S, Dihal K. Hopes and fears for intelligent machines in fiction and reality. Nat Mach Intell. 2019;1(2):74-78.
  • Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98.
  • Lal A, Nooruddin A, Umer F. Concerns regarding deployment of AI-based applications in dentistry: A review. BDJ Open. 2025;11(1):31.
  • Suárez A, Díaz-Flores García V, Algar J, et al. Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers. Int Endod J. 2024;57(1):108-113.
  • Jeong H, Park IS, Choi Y, et al. Korean dental hygiene students’ perceptions and attitudes toward artificial intelligence: An online survey. J Dent Educ. 2023;87(6):804-812.
  • Yüzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2020;84(5):556-562.
  • Elchaghaby M, Wahby R. Knowledge, attitudes, and perceptions of a group of Egyptian dental students toward artificial intelligence: A cross-sectional study. BMC Oral Health. 2025;25(1):11.
  • Qutieshat A, Al Rusheidi A, Al Ghammari S, et al. Comparative analysis of diagnostic accuracy in endodontic assessments: Dental students vs. artificial intelligence. Diagnosis. 2024;11(3):259-265.
  • Gilson A, Safranek CW, Huang T, et al. How does ChatGPT perform on the United States Medical Licensing Examination (USMLE)? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9(1):e45312.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Kurumları Yönetimi, Sağlık Hizmetleri ve Sistemleri (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Elif Ülkü 0009-0002-8174-1259

Burhan Can Çanakçi 0000-0001-8872-5513

Gönderilme Tarihi 19 Eylül 2025
Kabul Tarihi 26 Ocak 2026
Yayımlanma Tarihi 27 Nisan 2026
DOI https://doi.org/10.24938/kutfd.1787152
IZ https://izlik.org/JA46RY56ZJ
Yayımlandığı Sayı Yıl 2026 Cilt: 28 Sayı: 1

Kaynak Göster

APA Ülkü, E., & Çanakçi, B. C. (2026). DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY. The Journal of Kırıkkale University Faculty of Medicine, 28(1), 55-63. https://doi.org/10.24938/kutfd.1787152
AMA 1.Ülkü E, Çanakçi BC. DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY. Kırıkkale Üni Tıp Derg. 2026;28(1):55-63. doi:10.24938/kutfd.1787152
Chicago Ülkü, Elif, ve Burhan Can Çanakçi. 2026. “DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY”. The Journal of Kırıkkale University Faculty of Medicine 28 (1): 55-63. https://doi.org/10.24938/kutfd.1787152.
EndNote Ülkü E, Çanakçi BC (01 Nisan 2026) DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY. The Journal of Kırıkkale University Faculty of Medicine 28 1 55–63.
IEEE [1]E. Ülkü ve B. C. Çanakçi, “DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY”, Kırıkkale Üni Tıp Derg, c. 28, sy 1, ss. 55–63, Nis. 2026, doi: 10.24938/kutfd.1787152.
ISNAD Ülkü, Elif - Çanakçi, Burhan Can. “DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY”. The Journal of Kırıkkale University Faculty of Medicine 28/1 (01 Nisan 2026): 55-63. https://doi.org/10.24938/kutfd.1787152.
JAMA 1.Ülkü E, Çanakçi BC. DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY. Kırıkkale Üni Tıp Derg. 2026;28:55–63.
MLA Ülkü, Elif, ve Burhan Can Çanakçi. “DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY”. The Journal of Kırıkkale University Faculty of Medicine, c. 28, sy 1, Nisan 2026, ss. 55-63, doi:10.24938/kutfd.1787152.
Vancouver 1.Elif Ülkü, Burhan Can Çanakçi. DENTAL STUDENTS’ KNOWLEDGE AND AWARENESS OF ARTIFICIAL INTELLIGENCE: A CROSS-SECTIONAL STUDY AT TRAKYA UNIVERSITY. Kırıkkale Üni Tıp Derg. 01 Nisan 2026;28(1):55-63. doi:10.24938/kutfd.1787152

Bu Dergi, Kırıkkale Üniversitesi Tıp Fakültesi Yayınıdır.