Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
ChatGPT Responses to Frequently Asked Questions on Ménière's Disease: A Comparison to Clinical Practice Guideline Answers
11
Zitationen
4
Autoren
2024
Jahr
Abstract
Abstract Objective Evaluate the quality of responses from Chat Generative Pre‐Trained Transformer (ChatGPT) models compared to the answers for “Frequently Asked Questions” (FAQs) from the American Academy of Otolaryngology–Head and Neck Surgery (AAO‐HNS) Clinical Practice Guidelines (CPG) for Ménière's disease (MD). Study Design Comparative analysis. Setting The AAO‐HNS CPG for MD includes FAQs that clinicians can give to patients for MD‐related questions. The ability of ChatGPT to properly educate patients regarding MD is unknown. Methods ChatGPT‐3.5 and 4.0 were each prompted with 16 questions from the MD FAQs. Each response was rated in terms of (1) comprehensiveness, (2) extensiveness, (3) presence of misleading information, and (4) quality of resources. Readability was assessed using Flesch‐Kincaid Grade Level (FKGL) and Flesch Reading Ease Score (FRES). Results ChatGPT‐3.5 was comprehensive in 5 responses whereas ChatGPT‐4.0 was comprehensive in 9 (31.3% vs 56.3%, P = .2852). ChatGPT‐3.5 and 4.0 were extensive in all responses ( P = 1.0000). ChatGPT‐3.5 was misleading in 5 responses whereas ChatGPT‐4.0 was misleading in 3 (31.3% vs 18.75%, P = .6851). ChatGPT‐3.5 had quality resources in 10 responses whereas ChatGPT‐4.0 had quality resources in 16 (62.5% vs 100%, P = .0177). AAO‐HNS CPG FRES (62.4 ± 16.6) demonstrated an appropriate readability score of at least 60, while both ChatGPT‐3.5 (39.1 ± 7.3) and 4.0 (42.8 ± 8.5) failed to meet this standard. All platforms had FKGL means that exceeded the recommended level of 6 or lower. Conclusion While ChatGPT‐4.0 had significantly better resource reporting, both models have room for improvement in being more comprehensive, more readable, and less misleading for patients.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.740 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.649 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.202 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.886 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.