Role-based evaluation of artificial intelligence chatbot responses in orthodontic emergency scenarios: accuracy, readability, understandability, and patient-oriented communication

OBJECTIVES: This study investigated role-based differences in the accuracy, readability, understandability, and internal consistency of chatbot responses to orthodontic emergencies and examined the clinical implications of patient-oriented communication. METHODS: Twenty-three standardized orthodontic emergency scenarios were presented to four chatbots- ChatGPT-4o, Claude 3 Opus, Microsoft Copilot, and Gemini 2.5-using patient and orthodontist roles. Response accuracy was evaluated by expert orthodontists and research assistants using a 3-point Likert scale, while readability, understandability, and internal consistency were assessed with the Atesman index, Sonmez formula, and Cronbach's α. Patient-role responses were descriptively analyzed using predefined communication dimensions to contextualize quantitative findings. RESULTS: Significant chatbot-specific role-based differences were observed, with Claude 3 Opus (p = 0.001) and Gemini 2.5 (p = 0.023) showing higher accuracy in the orthodontist role. In the patient role, ChatGPT-4o and Claude 3 Opus showed the highest rates of correct information, while Claude 3 Opus had the highest rate in the orthodontist role. Patient-role responses were significantly more understandable than orthodontist-role responses (p < 0.05). ChatGPT-4o (α = 0.862) and Gemini 2.5 (α = 0.815) showed high internal consistency. Qualitative analysis indicated that patient-oriented responses frequently adopted a reassuring tone and emphasized temporary self-care strategies, potentially influencing perceived urgency in orthodontic emergencies. CONCLUSIONS: Chatbot performance varied according to user role. Patient-oriented responses were more understandable despite similar readability across models and could influence perceptions of urgency and professional responsibility, highlighting the need for cautious framing of chatbot-generated information for orthodontic emergency guidance. CLINICAL RELEVANCE: Chatbots can provide preliminary information in orthodontic emergencies; however, due to limitations in accuracy and consistency, they should be used only as supportive tools and should not replace professional clinical judgment.

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Publication Details

Journal
BMC Oral Health
Published
2026-06-08
DOI
https://doi.org/10.1186/s12903-026-08836-z
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

Role-based evaluation of artificial intelligence chatbot responses in orthodontic emergency scenarios: accuracy, readability, understandability, and patient-oriented communication

Mustafa Özdemir, Aysegul Gulec
BMC Oral Health
Artificial Intelligence in Healthcare and Education
article

Role-based evaluation of artificial intelligence chatbot responses in orthodontic emergency scenarios: accuracy, readability, understandability, and patient-oriented communication

Mustafa Özdemir, Aysegul Gulec
article en

Abstract

OBJECTIVES: This study investigated role-based differences in the accuracy, readability, understandability, and internal consistency of chatbot responses to orthodontic emergencies and examined the clinical implications of patient-oriented communication. METHODS: Twenty-three standardized orthodontic emergency scenarios were presented to four chatbots- ChatGPT-4o, Claude 3 Opus, Microsoft Copilot, and Gemini 2.5-using patient and orthodontist roles. Response accuracy was evaluated by expert orthodontists and research assistants using a 3-point Likert scale, while readability, understandability, and internal consistency were assessed with the Atesman index, Sonmez formula, and Cronbach's α. Patient-role responses were descriptively analyzed using predefined communication dimensions to contextualize quantitative findings. RESULTS: Significant chatbot-specific role-based differences were observed, with Claude 3 Opus (p = 0.001) and Gemini 2.5 (p = 0.023) showing higher accuracy in the orthodontist role. In the patient role, ChatGPT-4o and Claude 3 Opus showed the highest rates of correct information, while Claude 3 Opus had the highest rate in the orthodontist role. Patient-role responses were significantly more understandable than orthodontist-role responses (p < 0.05). ChatGPT-4o (α = 0.862) and Gemini 2.5 (α = 0.815) showed high internal consistency. Qualitative analysis indicated that patient-oriented responses frequently adopted a reassuring tone and emphasized temporary self-care strategies, potentially influencing perceived urgency in orthodontic emergencies. CONCLUSIONS: Chatbot performance varied according to user role. Patient-oriented responses were more understandable despite similar readability across models and could influence perceptions of urgency and professional responsibility, highlighting the need for cautious framing of chatbot-generated information for orthodontic emergency guidance. CLINICAL RELEVANCE: Chatbots can provide preliminary information in orthodontic emergencies; however, due to limitations in accuracy and consistency, they should be used only as supportive tools and should not replace professional clinical judgment.

BMC Oral Health
Gaziantep University (TR)
Quality Education
Openalex Percentile: Top 8%
Artificial Intelligence in Healthcare and Education
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