Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluation of ChatGPT-4o’s answers to questions about hip arthroscopy from the patient perspective
14
Zitationen
5
Autoren
2024
Jahr
Abstract
OBJECTIVES: This study aimed to evaluate the responses provided by ChatGPT-4o to the most frequently asked questions by patients regarding hip arthroscopy. MATERIALS AND METHODS: In this cross-sectional survey study, a new Google account without a search history was created to determine the 20 most frequently asked questions about hip arthroscopy via Google. These questions were asked to a new ChatGPT-4o account on June 1, 2024, and the responses were recorded. Ten orthopedic surgeons specializing in sports surgery rated the responses using a rating scale to assess relevance, accuracy, clarity, and completeness. The responses were scored on a scale from 1 to 5, with 1 being the worst and 5 being the best. The interrater reliability assessed via the intraclass correlation coefficient (ICC). RESULTS: The lowest score given by the surgeons for any response was 4/5 in each subcategory. The highest mean scores were in accuracy and clarity, followed by relevance, with completeness receiving the lowest scores. The overall mean score was 4.49±0.16. Interrater reliability showed insufficient overall agreement (ICC=0.004, p=0.383), with the highest agreement in clarity (ICC=0.039, p=0.131) and the lowest in accuracy (ICC=-0.019, p=0.688). CONCLUSION: The study confirms our hypothesis that ChatGPT-4o provides above-average quality responses to frequently asked questions about hip arthroscopy, as evidenced by the high scores in relevance, accuracy, clarity, and completeness. However, it is still advisable to consult orthopedic specialists on the subject, incorporating ChatGPT's suggestions during the final decision-making process.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.557 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.447 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.944 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.