تقرير
Assessing the Usability of GutGPT: A Simulation Study of an AI Clinical Decision Support System for Gastrointestinal Bleeding Risk
العنوان: | Assessing the Usability of GutGPT: A Simulation Study of an AI Clinical Decision Support System for Gastrointestinal Bleeding Risk |
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المؤلفون: | Chan, Colleen, You, Kisung, Chung, Sunny, Giuffrè, Mauro, Saarinen, Theo, Rajashekar, Niroop, Pu, Yuan, Shin, Yeo Eun, Laine, Loren, Wong, Ambrose, Kizilcec, René, Sekhon, Jasjeet, Shung, Dennis |
سنة النشر: | 2023 |
المجموعة: | Computer Science Statistics |
مصطلحات موضوعية: | Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Applications |
الوصف: | Applications of large language models (LLMs) like ChatGPT have potential to enhance clinical decision support through conversational interfaces. However, challenges of human-algorithmic interaction and clinician trust are poorly understood. GutGPT, a LLM for gastrointestinal (GI) bleeding risk prediction and management guidance, was deployed in clinical simulation scenarios alongside the electronic health record (EHR) with emergency medicine physicians, internal medicine physicians, and medical students to evaluate its effect on physician acceptance and trust in AI clinical decision support systems (AI-CDSS). GutGPT provides risk predictions from a validated machine learning model and evidence-based answers by querying extracted clinical guidelines. Participants were randomized to GutGPT and an interactive dashboard, or the interactive dashboard and a search engine. Surveys and educational assessments taken before and after measured technology acceptance and content mastery. Preliminary results showed mixed effects on acceptance after using GutGPT compared to the dashboard or search engine but appeared to improve content mastery based on simulation performance. Overall, this study demonstrates LLMs like GutGPT could enhance effective AI-CDSS if implemented optimally and paired with interactive interfaces. Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10, 2023, New Orleans, United States, 11 pages |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2312.10072 |
رقم الأكسشن: | edsarx.2312.10072 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |