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A Content-Aware Chatbot based on GPT 4 provides trustworthy Recommendations for Cone Beam Computed Tomography Guidelines in Dental Imaging.

OBJECTIVES To develop a content-aware chatbot based on GPT-3.5-Turbo and GPT-4 with specialized knowledge on the German S2 Cone-Beam CT (CBCT) dental imaging guideline and to compare the performance against humans. METHODS The LlamaIndex software library was used to integrate the guideline context into the chatbots. Based on the CBCT S2 guideline, 40 questions were posed to content-aware chatbots and early career and senior practitioners with different levels of experience served as reference. The chatbots' performance was compared in terms of recommendation accuracy and explanation quality. Chi-square test and one-tailed Wilcoxon signed rank

Resource information

Authors
Russe, Maximilian Frederik, Rau, Alexander, Ermer, Michael Andreas, Rothweiler, René, Wenger, Sina, Klöble, Klara, Schulze, Ralf Kurt Willy, Bamberg, Fabian, Schmelzeisen, Rainer, Reisert, Marco, Semper-Hogg, Wiebke
Research area
Artificial intelligence
Research topics
Chatbot Cone-Beam CT Dental Imaging Natural Language Processing
Publication date
2024
DOI
10.48350/191322
Upstream last modified
Sep 7, 2026
License
open.access
Last record update
Nov 5, 2026

About this resource

OBJECTIVES To develop a content-aware chatbot based on GPT-3.5-Turbo and GPT-4 with specialized knowledge on the German S2 Cone-Beam CT (CBCT) dental imaging guideline and to compare the performance against humans. METHODS The LlamaIndex software library was used to integrate the guideline context into the chatbots. Based on the CBCT S2 guideline, 40 questions were posed to content-aware chatbots and early career and senior practitioners with different levels of experience served as reference. The chatbots' performance was compared in terms of recommendation accuracy and explanation quality. Chi-square test and one-tailed Wilcoxon signed rank test evaluated accuracy and explanation quality, respectively. RESULTS The GPT-4 based chatbot provided 100% correct recommendations and superior explanation quality compared to the one based on GPT3.5-Turbo (87.5% vs. 57.5% for GPT-3.5-Turbo; p = 0.003). Moreover, it outperformed early career practitioners in correct answers (p = 0.002 and p = 0.032) and earned higher trust than the chatbot using GPT-3.5-Turbo (p = 0.006). CONCLUSIONS A content-aware chatbot using GPT-4 reliably provided recommendations according to current consensus guidelines. The responses were deemed trustworthy and transparent and therefore facilitate the integration of artificial intelligence into clinical decision-making.