Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
ChatGPT Can Often Respond Adequately to Common Patient Questions Regarding Femoroacetabular Impingement
1
Zitationen
6
Autoren
2024
Jahr
Abstract
OBJECTIVE: This study aims to analyze the ability of ChatGPT to answer frequently asked questions (FAQs) regarding FAI. We hypothesize that ChatGPT can provide accurate and thorough responses when presented with FAQs regarding FAI. DESIGN: Ten FAQs regarding FAI were presented to ChatGPT 3.5 and initial responses were recorded and analyzed against evidence-based literature. Responses were rated as "excellent response requiring no further clarification," "satisfactory response requiring minimal clarification," "satisfactory response requiring moderate clarification," or "unsatisfactory response requiring substantial clarification." SETTING: Institutional. INDEPENDENT VARIABLES: Frequently asked questions regarding femoroacetabular impingement. MAIN OUTCOME MEASURES: Accuracy and thoroughness of ChatGPT responses to FAQs. Hypothesis was formulated before data collection. RESULTS: Most responses from ChatGPT were rated as satisfactory and required only minimal clarification. Two responses received an excellent rating and required no further clarification, while only 1 response from ChatGPT was rated unsatisfactory and required substantial clarification. CONCLUSIONS: ChatGPT provided largely accurate and thorough responses to FAQs regarding FAI while appropriately reiterating the importance of always consulting a medical professional.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.674 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.583 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.105 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.862 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.