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Does Sequential Use of Multiple Chatbots Influence Emergency Guidance for Dental Avulsion?
Mehmet Buldur1, Berkant Sezer2
1Department of Restorative Dentistry, School of Dentistry, Çanakkale Onsekiz Mart University, Çanakkale, Türkiye.
Summary
Sequential chatbot consultations for dental avulsion guidance did not improve information quality. AI chatbot response order had no significant effect on accuracy, safety, or completeness for dental emergency advice.
Area of Science:
- Artificial Intelligence in Dentistry
- Dental Traumatology
- Clinical Decision Support Systems
Background:
- AI chatbots are increasingly used for dental emergency guidance.
- The clinical value of sequential AI chatbot consultations is not well understood.
- Permanent tooth avulsion management requires accurate and timely information.
Purpose of the Study:
- To investigate if the order of responses from AI chatbots influences the quality of guidance for permanent tooth avulsion.
- To assess the impact of sequential consultation on accuracy, safety, and completeness of AI-generated dental emergency advice.
Main Methods:
- Eight key questions on avulsion management were developed based on International Association of Dental Traumatology guidelines.
- Three AI chatbots (ChatGPT, Gemini, DeepSeek) were queried using a sequential prompt design with all response order permutations.
- Expert dentists evaluated responses based on accuracy, safety, completeness, correction ability, clarity, and usefulness.
Main Results:
- No significant difference in composite quality scores was found based on chatbot response order.
- Sequential consultation did not consistently improve accuracy, safety, completeness, or correction ability.
- Response order effects were negligible across all tested AI chatbot platforms.
Conclusions:
- Sequential consultation of AI chatbots does not enhance the quality, safety, or guideline adherence for dental avulsion management.
- Response order alone has limited corrective impact on AI-generated dental emergency information under structured evaluation.
- Findings highlight the need for careful interpretation of AI-generated advice within experimental limitations.
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