ArticlePEC innovation2026
Accuracy and empathy of AI-based conversational chatbots in response to temporomandibular dysfunction related queries.
Article in PEC innovation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Objectives: To compare the accuracy and empathy of responses generated by artificial intelligence (AI)-based chatbots to commonly asked temporomandibular dysfunction (TMD)-related questions. Additionally, test the performance of an automated text-based empathy detection model against subject matter experts (SMEs) judgments. Materials and methods: TMD-related questions ( Results: DS generated responses with the highest word count (573.6 ± 132.7); significantly more than CG (263.4 ± 63.5) and CD (186.6 ± 25.6). DS also had the highest accuracy across all clinical domains. Overall accuracy of the responses generated by the three chatbots was high. However, variations in accuracy based on clinical domain of the question were observed. Empathy assessments revealed moderate reliability (correlation ∼0.6) among SMEs. The BERT model showed strong concordance with SME judgments for high-empathy responses but demonstrated lower agreement for low-empathy categorizations. Conclusion: AI chatbots show promise in providing accurate information regarding TMDs, but their ability to convey empathy remains limited. The observed differences in accuracy and empathy among the three AI chatbots examined are based on a limited dataset and should therefore be interpreted with caution. Current AI chatbots represent an intermediate stage of development, demonstrating adequate technical proficiency while remaining constrained in addressing the humanistic dimensions of patient care. Although empathy detection models may inform future development, significant challenges in empathetic communication persist.
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