Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Understanding the Landscape: The Emergence of Artificial Intelligence (AI), ChatGPT, and Google Bard in Gastroenterology
10
Zitationen
11
Autoren
2024
Jahr
Abstract
Introduction Artificial intelligence (AI) integration in healthcare, specifically in gastroenterology, has opened new avenues for enhanced patient care and medical decision-making. This study aims to assess the reliability and accuracy of two prominent AI tools, ChatGPT 4.0 and Google Bard, in answering gastroenterology-related queries, thereby evaluating their potential utility in medical settings. Methods The study employed a structured approach where typical gastroenterology questions were input into ChatGPT 4.0 and Google Bard. Independent reviewers evaluated responses using a Likert scale and cross-referenced them with guidelines from authoritative gastroenterology bodies. Statistical analysis, including the Mann-Whitney U test, was conducted to assess the significance of differences in ratings. Results ChatGPT 4.0 demonstrated higher reliability and accuracy in its responses than Google Bard, as indicated by higher mean ratings and statistically significant p-values in hypothesis testing. However, limitations in the data structure, such as the inability to conduct detailed correlation analysis, were noted. Conclusion The study concludes that ChatGPT 4.0 outperforms Google Bard in providing reliable and accurate responses to gastroenterology-related queries. This finding underscores the potential of AI tools like ChatGPT in enhancing healthcare delivery. However, the study also highlights the need for a broader and more diverse assessment of AI capabilities in healthcare to leverage their potential in clinical practice fully.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.418 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.288 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.726 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.516 Zit.