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Korean J Orthod
Copyright © The Korean Association of Orthodontists.
Betul Kulaa, Ahmet Kulab, Fatih Bagcierc, Bulent Alyanakd
aAssistant Professor, Department of Orthodontics, Faculty of Dentistry, Istanbul Galata University, Istanbul
e-mail: betul.kula@galata.edu.tr
ORCID: 0000-0001-5661-0762
bAssistant Professor, Department of Prosthodontics, Faculty of Dentistry, Uskudar University, Istanbul
e-mail: ahmet.kula@uskudar.edu.tr
ORCID: 0000-0002-8266-9968
cAssociate Professor, Clinic of Physical Medicine and Rehabilitation, Cam and Sakura City Hospital, Istanbul
e-mail: bagcier_42@hotmail.com
ORCID: 0000-0002-6103-7873
dMedical Doctor, Department of Physical Medicine and Rehabilitation, Golcuk Necati Celik State Hospital, Kocaeli
e-mail: bulentalyanak@hotmail.com
ORCID: 0000-0003-4295-4286
Correspondence to:Author: Betul Kula.
Assistant Professor, Department of Orthodontics, Faculty of Dentistry, Istanbul Galata University, Evliya Celebi Street, Mesrutiyet Boulevard No:62, 34430, Istanbul, Turkey.
e-mail: betul.kula@galata.edu.tr
Phone number: +905543505571
Objectives: The aim of this study was to evaluate the reliability and usefulness of information generated by Chat Generative Pre-Trained Transformer (ChatGPT) on Temporomandibular joint disorders (TMD).
Methods: We asked ChatGPT about the diseases specified in the TMD classification and scored the responses using the Likert reliability and usefulness scales, the modified DISCERN test (mD) and the Global Quality Scale (GQS).
Results: The highest Likert scores for both reliability and usefulness were for masticatory muscle disorders (mean ± SD: 6 + 0) and the lowest scores were for inflammatory disorders of the TMJ (mean ± SD: 4.3 + 0.6 for reliability, mean ± SD: 4 + 0 for usefulness). The median Likert reliability score indicated that the responses were highly reliable. The median Likert usefulness score was 5(4-6) and indicating that the responses were moderately useful. A comparative analysis was performed, and no statistically significant difference was found in any subject for both reliability and usefulness (p= 0.083–1.00, respectively). The median mDISCERN score was 4 (3-5) for the two researchers. A statistically significant difference was observed in the mean mDISCERN scores between the two researchers (P=0.046). The GQS scores indicated a moderate to high quality (mean ± SD: 3.8 ± 0.8 for researcher 1, mean ± SD: 4 ± 0.5 for researcher 2). No statistically significant correlation was found between mDISCERN and GQS scores (r=-0.006, p=0.980).
Conclusion: Although ChatGPT-4 has significant potential, it can be used as an additional source of information for patients and clinicians about TMD.
Keywords: artificial intelligence, ChatGPT, temporomandibular joint disorders
Korean J Orthod
First Published Date December 11, 2024
Copyright © The Korean Association of Orthodontists.
Betul Kulaa, Ahmet Kulab, Fatih Bagcierc, Bulent Alyanakd
aAssistant Professor, Department of Orthodontics, Faculty of Dentistry, Istanbul Galata University, Istanbul
e-mail: betul.kula@galata.edu.tr
ORCID: 0000-0001-5661-0762
bAssistant Professor, Department of Prosthodontics, Faculty of Dentistry, Uskudar University, Istanbul
e-mail: ahmet.kula@uskudar.edu.tr
ORCID: 0000-0002-8266-9968
cAssociate Professor, Clinic of Physical Medicine and Rehabilitation, Cam and Sakura City Hospital, Istanbul
e-mail: bagcier_42@hotmail.com
ORCID: 0000-0002-6103-7873
dMedical Doctor, Department of Physical Medicine and Rehabilitation, Golcuk Necati Celik State Hospital, Kocaeli
e-mail: bulentalyanak@hotmail.com
ORCID: 0000-0003-4295-4286
Correspondence to:Author: Betul Kula.
Assistant Professor, Department of Orthodontics, Faculty of Dentistry, Istanbul Galata University, Evliya Celebi Street, Mesrutiyet Boulevard No:62, 34430, Istanbul, Turkey.
e-mail: betul.kula@galata.edu.tr
Phone number: +905543505571
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objectives: The aim of this study was to evaluate the reliability and usefulness of information generated by Chat Generative Pre-Trained Transformer (ChatGPT) on Temporomandibular joint disorders (TMD).
Methods: We asked ChatGPT about the diseases specified in the TMD classification and scored the responses using the Likert reliability and usefulness scales, the modified DISCERN test (mD) and the Global Quality Scale (GQS).
Results: The highest Likert scores for both reliability and usefulness were for masticatory muscle disorders (mean ± SD: 6 + 0) and the lowest scores were for inflammatory disorders of the TMJ (mean ± SD: 4.3 + 0.6 for reliability, mean ± SD: 4 + 0 for usefulness). The median Likert reliability score indicated that the responses were highly reliable. The median Likert usefulness score was 5(4-6) and indicating that the responses were moderately useful. A comparative analysis was performed, and no statistically significant difference was found in any subject for both reliability and usefulness (p= 0.083–1.00, respectively). The median mDISCERN score was 4 (3-5) for the two researchers. A statistically significant difference was observed in the mean mDISCERN scores between the two researchers (P=0.046). The GQS scores indicated a moderate to high quality (mean ± SD: 3.8 ± 0.8 for researcher 1, mean ± SD: 4 ± 0.5 for researcher 2). No statistically significant correlation was found between mDISCERN and GQS scores (r=-0.006, p=0.980).
Conclusion: Although ChatGPT-4 has significant potential, it can be used as an additional source of information for patients and clinicians about TMD.
Keywords: artificial intelligence, ChatGPT, temporomandibular joint disorders
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