Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Comparative evaluation of ChatGPT, Gemini, and Grok in clinical decision-making and general knowledge assessment for impacted maxillary canines
0
Zitationen
2
Autoren
2025
Jahr
Abstract
Objective: This study aimed to compare extraction versus orthodontic eruption decisions for impacted maxillary canines made by three artificial intelligence-based chatbots (ChatGPT, Gemini, and Grok) with those made by orthodontist raters, and to evaluate the overall accuracy of these artificial intelligence-generated recommendations. Methods: Thirty-three patients with impacted maxillary canines were selected, and standardized case scenarios incorporating key diagnostic parameters were presented to the three chatbots. Their treatment decisions were recorded and compared with orthodontists' consensus decisions. Additionally, 10 general queries regarding impacted maxillary canines were submitted to the chatbots. The responses were rated by three orthodontists using a modified 5-point Global Quality Score. Results: = 0.006). Conclusions: While Gemini showed lower clinical alignment with orthodontists for treatment decisions regarding impacted canines, ChatGPT and Grok demonstrated moderate agreement with orthodontists and produced relatively accurate responses. These findings highlight the potential of chatbots as supportive tools for orthodontic decision-making. However, their use requires careful supervision to avoid the risks associated with inaccurate or misleading recommendations.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.561 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.452 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.948 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.797 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.