Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Comparative Analysis of Artificial Intelligence Chatbots for Heart Failure Care
0
Zitationen
5
Autoren
2026
Jahr
Abstract
Heart failure is a prevalent condition associated with high morbidity and mortality. The rapid evolution and widespread adoption of artificial intelligence applications have significantly advanced in the field of medicine. We aim to assess the value and reliability of ChatGPT 4o, Gemini, and Bing Chat’s responses regarding heart failure. A list of fifty commonly asked questions regarding heart failure was asked twice to ChatGPT 4o, Gemini, and Bing Chat. Two experienced cardiologists assessed each the natural language processing models’ answers without knowing each other’s scores. The content of answers was evaluated using the following scale: correct (1), partially correct (2), a mix of accurate and inaccurate (3) and completely inaccurate (4). Most answers were correct or partially correct; only Bing Chat gave some inaccurate responses, unlike ChatGPT-4o and Gemini. In terms of ‘correct’ answers, ChatGPT scored 88%, Gemini scored 70%, and Bing Chat scored 64%. ChatGPT 4o provided the highest ‘reproducible’ score at 88%, followed by Gemini at 86% and Bing Chat at 72%. This study demonstrated that ChatGPT 4o, in particular, has the ability to produce valuable responses to patient inquiries regarding heart failure. In the future, as chatbots are further investigated and improved, these models may have the potential to be utilized by both patients and healthcare professionals for managing chronic conditions like heart failure.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.707 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.613 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.159 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.875 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.