Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluating the Accuracy, Reliability, Consistency, and Readability of Different Large Language Models in Restorative Dentistry
21
Zitationen
2
Autoren
2025
Jahr
Abstract
OBJECTIVE: This study aimed to evaluate the reliability, consistency, and readability of responses provided by various artificial intelligence (AI) programs to questions related to Restorative Dentistry. MATERIALS AND METHODS: Forty-five knowledge-based information and 20 questions (10 patient-related and 10 dentistry-specific) were posed to ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, Chatsonic, Copilot, and Gemini Advanced chatbots. The DISCERN questionnaire was used to assess the reliability; Flesch Reading Ease and Flesch-Kincaid Grade Level scores were utilized to evaluate readability. Accuracy and consistency were determined based on the chatbots' responses to the knowledge-based questions. RESULTS: ChatGPT-4, ChatGPT-4o, Chatsonic, and Copilot demonstrated "good" reliability, while ChatGPT-3.5 and Gemini Advanced showed "fair" reliability. Chatsonic exhibited the highest "DISCERN total score" for patient-related questions, while ChatGPT-4o performed best for dentistry-specific questions. No significant differences were found in readability among the chatbots (p > 0.05). ChatGPT-4o showed the highest accuracy (93.3%) for knowledge-based questions, while Copilot had the lowest (68.9%). ChatGPT-4 demonstrated the highest consistency between repetitions. CONCLUSION: Performance of AIs varied in terms of accuracy, reliability, consistency, and readability when responding to Restorative Dentistry questions. ChatGPT-4o and Chatsonic showed promising results for academic and patient education applications. However, the readability of responses was generally above recommended levels for patient education materials. CLINICAL SIGNIFICANCE: The utilization of AI has an increasing impact on various aspects of dentistry. Moreover, if the responses to patient-related and dentistry-specific questions in restorative dentistry prove to be reliable and comprehensible, this may yield promising outcomes for the future.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.646 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.554 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.071 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.851 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.