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The performance of artificial intelligence models in generating responses to general orthodontic questions: ChatGPT vs Google Bard.
Daraqel, Baraa; Wafaie, Khaled; Mohammed, Hisham; Cao, Li; Mheissen, Samer; Liu, Yang; Zheng, Leilei.
Afiliación
  • Daraqel B; Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China; Oral Health Research and Promotion Unit, Al-Qu
  • Wafaie K; Department of Orthodontics, Faculty of Dentistry, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Mohammed H; Private practice, Cairo, Egypt.
  • Cao L; Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China.
  • Mheissen S; Private practice, Damascus, Syria.
  • Liu Y; Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China.
  • Zheng L; Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China. Electronic address: zhengleileicqmu@hospital.c
Am J Orthod Dentofacial Orthop ; 165(6): 652-662, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38493370
ABSTRACT

INTRODUCTION:

This study aimed to evaluate and compare the performance of 2 artificial intelligence (AI) models, Chat Generative Pretrained Transformer-3.5 (ChatGPT-3.5; OpenAI, San Francisco, Calif) and Google Bidirectional Encoder Representations from Transformers (Google Bard; Bard Experiment, Google, Mountain View, Calif), in terms of response accuracy, completeness, generation time, and response length when answering general orthodontic questions.

METHODS:

A team of orthodontic specialists developed a set of 100 questions in 10 orthodontic domains. One author submitted the questions to both ChatGPT and Google Bard. The AI-generated responses from both models were randomly assigned into 2 forms and sent to 5 blinded and independent assessors. The quality of AI-generated responses was evaluated using a newly developed tool for accuracy of information and completeness. In addition, response generation time and length were recorded.

RESULTS:

The accuracy and completeness of responses were high in both AI models. The median accuracy score was 9 (interquartile range [IQR] 8-9) for ChatGPT and 8 (IQR 8-9) for Google Bard (Median difference 1; P <0.001). The median completeness score was similar in both models, with 8 (IQR 8-9) for ChatGPT and 8 (IQR 7-9) for Google Bard. The odds of accuracy and completeness were higher by 31% and 23% in ChatGPT than in Google Bard. Google Bard's response generation time was significantly shorter than that of ChatGPT by 10.4 second/question. However, both models were similar in terms of response length generation.

CONCLUSIONS:

Both ChatGPT and Google Bard generated responses were rated with a high level of accuracy and completeness to the posed general orthodontic questions. However, acquiring answers was generally faster using the Google Bard model.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ortodoncia / Inteligencia Artificial Límite: Humans Idioma: En Revista: Am J Orthod Dentofacial Orthop Asunto de la revista: ODONTOLOGIA / ORTODONTIA Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ortodoncia / Inteligencia Artificial Límite: Humans Idioma: En Revista: Am J Orthod Dentofacial Orthop Asunto de la revista: ODONTOLOGIA / ORTODONTIA Año: 2024 Tipo del documento: Article