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Assessing ChatGPT4 with and without retrieval augmented generation in anticoagulation management for gastrointestinal procedures

2024·7 Zitationen·Annals of GastroenterologyOpen Access
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7

Zitationen

1

Autoren

2024

Jahr

Abstract

Background: In view of the growing complexity of managing anticoagulation for patients undergoing gastrointestinal (GI) procedures, this study evaluated ChatGPT-4's ability to provide accurate medical guidance, comparing it with its prior artificial intelligence (AI) models (ChatGPT-3.5) and the retrieval-augmented generation (RAG)-supported model (ChatGPT4-RAG). Methods: Thirty-six anticoagulation-related questions, based on professional guidelines, were answered by ChatGPT-4. Nine gastroenterologists assessed these responses for accuracy and relevance. ChatGPT-4's performance was also compared to that of ChatGPT-3.5 and ChatGPT4-RAG. Additionally, a survey was conducted to understand gastroenterologists' perceptions of ChatGPT-4. Results: ChatGPT-4's responses showed significantly better accuracy and coherence compared to ChatGPT-3.5, with 30.5% of responses fully accurate and 47.2% generally accurate. ChatGPT4-RAG demonstrated a higher ability to integrate current information, achieving 75% full accuracy. Notably, for diagnostic and therapeutic esophagogastroduodenoscopy, 51.8% of responses were fully accurate; for endoscopic retrograde cholangiopancreatography with and without stent placement, 42.8% were fully accurate; and for diagnostic and therapeutic colonoscopy, 50% were fully accurate. Conclusions: ChatGPT4-RAG significantly advances anticoagulation management in endoscopic procedures, offering reliable and precise medical guidance. However, medicolegal considerations mean that a 75% full accuracy rate remains inadequate for independent clinical decision-making. AI may be more appropriately utilized to support and confirm clinicians' decisions, rather than replace them. Further evaluation is essential to maintain patient confidentiality and the integrity of the physician-patient relationship.

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Artificial Intelligence in Healthcare and EducationTopic ModelingRadiology practices and education
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