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| http://dx.doi.org/10.1001/jamanetworkopen.2023.27647 | DOI Listing |
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J Pediatr Orthop
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Department of Pediatric Orthopaedic Surgery, Hospital for Special Surgery, New York, NY.
Objective: Artificial intelligence (AI) chatbots, including chat generative pretrained transformer (ChatGPT) and Google Gemini, have significantly increased access to medical information. However, in pediatric orthopaedics, no study has evaluated the accuracy of AI chatbots compared with evidence-based recommendations, including the American Academy of Orthopaedic Surgeons clinical practice guidelines (AAOS CPGs). The aims of this study were to compare responses by ChatGPT-4.
View Article and Find Full Text PDFEvaluation of different artificial intelligence applications in responding to regenerative endodontic procedures.
BMC Oral Health
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Department of Endodontics, Faculty of Dentistry, Marmara University, Başıbüyük, Başıbüyük Yolu Marmara Üniversitesi Başıbüyük Sağlık Yerleşkesi 9/3, Başıbüyük - Maltepe, PO Box: 34854, İstanbul, Turkey.
Introduction: The integration of artificial intelligence (AI) technologies in healthcare is revolutionizing the workflows of healthcare professionals, enabling faster and more accurate patient treatment. This study aims to evaluate the accuracy of responses provided by different AI chatbots to questions that dentists might ask regarding regenerative endodontic treatment (RET), a procedure that shows promising biological healing potential.
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Evaluation of a context-aware chatbot using retrieval-augmented generation for answering clinical questions on medication-related osteonecrosis of the jaw.
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Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
The potential of large language models (LLMs) in medical applications is significant, and Retrieval-augmented generation (RAG) can address the weaknesses of these models in terms of data transparency and scientific accuracy by incorporating current scientific knowledge into responses. In this study, RAG and GPT-4 by OpenAI were applied to develop GuideGPT, a context aware chatbot integrated with a knowledge database from 449 scientific publications designed to provide answers on the prevention, diagnosis, and treatment of medication-related osteonecrosis of the jaw (MRONJ). A comparison was made with a generic LLM ("PureGPT") across 30 MRONJ-related questions.
View Article and Find Full Text PDFComparative Evaluation of Chatbot Responses on Coronary Artery Disease.
Turk Kardiyol Dern Ars
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Department of Cardiology, Dr Siyami Ersek Thoracic and Cardiovascular Surgery Training Hospital, İstanbul, Türkiye.
Objective: Coronary artery disease (CAD) is the leading cause of morbidity and mortality globally. The growing interest in natural language processing chatbots (NLPCs) has driven their inevitable widespread adoption in healthcare. The purpose of this study was to evaluate the accuracy and reproducibility of responses provided by NLPCs, such as ChatGPT, Gemini, and Bing, to frequently asked questions about CAD.
View Article and Find Full Text PDFEvaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval.
Sci Rep
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Department of Engineering, iHealth Labs, Sunnyvale, CA, 94085, United States.
Large language models (LLMs) are fundamentally transforming human-facing applications in the health and well-being domains: boosting patient engagement, accelerating clinical decision-making, and facilitating medical education. Although state-of-the-art LLMs have shown superior performance in several conversational applications, evaluations within nutrition and diet applications are still insufficient. In this paper, we propose to employ the Registered Dietitian (RD) exam to conduct a standard and comprehensive evaluation of state-of-the-art LLMs, GPT-4o, Claude 3.
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