Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Can Natural Language Processing (NLP) Provide Consultancy to Patients About Edentulism Teeth Treatment?
1
Zitationen
1
Autoren
2024
Jahr
Abstract
Aim This study aimed to evaluate the accuracy and quality of the answers given by artificial intelligence (AI) applications to the questions directed at tooth deficiency treatments. Materials and methods Fifteen questions asked by patients/ordinary people about missing tooth treatment were selected from the Quora platform. Questions were asked to the ChatGPT-4 (OpenAI Inc., San Francisco, California, United States) and Copilot (Microsoft Corporation, Redmond, Washington, United States) models. Responses were assessed by two expert physicians using a five-point Likert scale (LS) for accuracy and the Global Quality Scale (GQS) for quality. To assess the internal consistency and inter-rater agreement of ChatGPT-4 and Copilot, Cronbach's alpha, Spearman-Brown's coefficient, and Guttman's split-half coefficient were calculated to measure the reliability and internal consistency of both instruments (α=0.05). Results Copilot showed a mean LS value of 3.83±0.36 and ChatGPT-4 showed a lower mean value of 3.93±0.32. ChatGPT-4's GQS mean value (3.9±0.28) is also higher than Copilot (3.83±0.06) (p<0.001). Conclusion It can be said that AI chatbots gave highly accurate and consistent answers to questions about the treatment of toothlessness. With the ever-developing technology, AI chatbots can be used as consultants for dental treatments in the future.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.644 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.550 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.061 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.850 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.