Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial Intelligence Chatbots as Sources of Implant Dentistry Information for the Public: Validity and Reliability Assessment
2
Zitationen
5
Autoren
2025
Jahr
Abstract
This study assessed the reliability and validity of responses from three chatbot systems-OpenAI's GPT-3.5, Gemini, and Copilot-concerning frequently asked questions (FAQs) in implant dentistry posed by patients.Twenty FAQs were prompted to three chatbots in three different times utilizing their respective application programming interfaces. The responses were assessed for validity (low and high threshold) and reliability by two prosthodontic consultants using a five-point Likert scale.The test of normality was utilized using the Shapiro-Wilk test. Differences between different chatbots regarding the quantitative variables in a given (fixed) time point and between the same chatbots in different time points were assessed using Friedman's two-way analysis of variance by ranks, followed by pairwise comparisons. All statistical analyses were conducted using the SPSS (Statistical Package for Social Sciences) Version 26.0 software program.GPT-3.5 provided the longest responses, while Gemini was the most concise. All chatbots advised consulting dental professionals more frequently. Validity was high under the low-threshold test but low under the high-threshold test, with Copilot scoring the highest. Reliability was high for all, with Gemini achieving perfect consistency.Chatbots showed consistent and generally valid responses with some variability in accuracy and details. While the chatbots demonstrated a high degree of reliability, their validity-especially under high-threshold criterion-remains limited. Improvements in accuracy and comprehensiveness are necessary for more effective use in providing information about dental implants.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.336 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.207 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.607 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.476 Zit.