Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neurosurgeons frequently encounter and has acute, subacute and chronic forms. The incidence in adults is reported to be 1.72-20.60/100.000 people annually. Our study aimed to evaluate the quality, reliability and readability of the answers to questions asked to ChatGPT, Bard, and perplexity about "Subdural Hematoma." In this observational and cross-sectional study, we asked ChatGPT, Bard, and perplexity to provide the 100 most frequently asked questions about "Subdural Hematoma" separately. Responses from both chatbots were analyzed separately for readability, quality, reliability and adequacy. When the median readability scores of ChatGPT, Bard, and perplexity answers were compared with the sixth-grade reading level, a statistically significant difference was observed in all formulas (P < .001). All 3 chatbot responses were found to be difficult to read. Bard responses were more readable than ChatGPT's (P < .001) and perplexity's (P < .001) responses for all scores evaluated. Although there were differences between the results of the evaluated calculators, perplexity's answers were determined to be more readable than ChatGPT's answers (P < .05). Bard answers were determined to have the best GQS scores (P < .001). Perplexity responses had the best Journal of American Medical Association and modified DISCERN scores (P < .001). ChatGPT, Bard, and perplexity's current capabilities are inadequate in terms of quality and readability of "Subdural Hematoma" related text content. The readability standard for patient education materials as determined by the American Medical Association, National Institutes of Health, and the United States Department of Health and Human Services is at or below grade 6. The readability levels of the responses of artificial intelligence applications such as ChatGPT, Bard, and perplexity are significantly higher than the recommended 6th grade level.
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http://dx.doi.org/10.1097/MD.0000000000038009 | DOI Listing |
J Pers Med
December 2024
Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark.
Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology.
View Article and Find Full Text PDFJMIR Form Res
December 2024
Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany.
Background: The rapid development of large language models (LLMs) such as OpenAI's ChatGPT has significantly impacted medical research and education. These models have shown potential in fields ranging from radiological imaging interpretation to medical licensing examination assistance. Recently, LLMs have been enhanced with image recognition capabilities.
View Article and Find Full Text PDFWorld J Methodol
December 2024
Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India.
Background: Medication errors, especially in dosage calculation, pose risks in healthcare. Artificial intelligence (AI) systems like ChatGPT and Google Bard may help reduce errors, but their accuracy in providing medication information remains to be evaluated.
Aim: To evaluate the accuracy of AI systems (ChatGPT 3.
Cureus
December 2024
Oral and Maxillofacial Surgery, Kings College Hospital, London, GBR.
Background It is recognised that large language models (LLMs) may aid medical education by supporting the understanding of explanations behind answers to multiple choice questions. This study aimed to evaluate the efficacy of LLM chatbots ChatGPT and Bard in answering an Intermediate Life Support pre-course multiple choice question (MCQs) test developed by the Resuscitation Council UK focused on managing deteriorating patients and identifying causes and treating cardiac arrest. We assessed the accuracy of responses and quality of explanations to evaluate the utility of the chatbots.
View Article and Find Full Text PDFJ Am Acad Orthop Surg
December 2024
From the University of California, Davis, Sacramento, CA (Simister, Le, Meehan, Leshikar, Saiz, and Lum), the San Joaquin General Hospital, French Camp, CA (Huish), the Cedars Sinai, Los Angeles, CA (Tsai), and the Yale University, New Haven, CT (Halim and Tuason).
Introduction: The introduction of generative artificial intelligence (AI) may have a profound effect on residency applications. In this study, we explore the abilities of AI-generated letters of recommendation (LORs) by evaluating the accuracy of orthopaedic surgery residency selection committee members to identify LORs written by human or AI authors.
Methods: In a multicenter, single-blind trial, a total of 45 LORs (15 human, 15 ChatGPT, and 15 Google BARD) were curated.
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