Background Health literacy empowers patients to participate in their own healthcare. Personal health literacy is one's ability to find, understand, and use information/resources to make well-informed health decisions. Artificial intelligence (AI) has become a source for the acquisition of health-related information through large language model (LLM)-driven chatbots. Assessment of the readability and quality of health information produced by these chatbots has been the subject of numerous studies to date. This study seeks to assess the quality of patient education materials on cardiac catheterization produced by AI chatbots. Methodology We asked a set of 10 questions about cardiac catheterization to four chatbots: ChatGPT (OpenAI, San Francisco, CA), Microsoft Copilot (Microsoft Corporation, Redmond, WA), Google Gemini (Google DeepMind, London, UK), and Meta AI (Meta, New York, NY). The questions and subsequent answers were utilized to make patient education materials on cardiac catheterization. The quality of these materials was assessed using two validated instruments for patient education materials: DISCERN and the Patient Education Materials Assessment Tool (PEMAT). Results The overall DISCERN scores were 4.5 for ChatGPT, 4.4 for Microsoft Copilot and Google Gemini, and 3.8 for Meta AI. ChatGPT, Microsoft Copilot, and Google Gemini tied for the highest reliability score at 4.6, while Meta AI had the lowest with 4.2. ChatGPT had the highest quality score at 4.4, while Meta AI had the lowest with 3.4. ChatGPT and Google Gemini had Understandability scores of 100%, while Meta AI had the lowest with 82%. ChatGPT, Microsoft Copilot, and Google Gemini all had Actionability scores of 75%, while Meta AI had one of 50%. Conclusions ChatGPT produced the most reliable and highest quality materials, followed closely by Google Gemini. Meta AI produced the lowest quality materials. Given the easy accessibility that chatbots provide patients and the high-quality responses that we obtained, they could be a reliable source for patients to obtain information about cardiac catheterization.
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http://dx.doi.org/10.7759/cureus.69996 | DOI Listing |
Am J Sports Med
January 2025
Orthopaedic Surgery, Weill Medical College of Cornell University, New York, New York, USA.
Background: Microfragmented adipose tissue has been proposed for intra-articular treatment of knee osteoarthritis. There are little data comparing the outcomes of treatment between microfragmented adipose tissue and other biological treatments.
Purpose: To perform a systematic review and meta-analysis comparing microfragmented aspirated fat injections to other orthobiologics, hyaluronic acid, and corticosteroid injections for symptomatic knee osteoarthritis.
Background: Large language models (LLMs) offer opportunities to enhance radiological applications, but their performance in handling complex tasks remains insufficiently investigated.
Purpose: To evaluate the performance of LLMs integrated with Contrast-enhanced Ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) in diagnosing small (≤20mm) hepatocellular carcinoma (sHCC) in high-risk patients.
Materials And Methods: From November 2014 to December 2023, high-risk HCC patients with untreated small (≤20mm) focal liver lesions (sFLLs), were included in this retrospective study.
Cureus
November 2024
Emergency Medicine, Valaichchenai Base Hospital, Valaichchenai, LKA.
Introduction: Artificial intelligence (AI) plays a significant role in creating brochures on radiological procedures for patient education. Thus, this study aimed to evaluate the responses generated by ChatGPT (San Francisco, CA: OpenAI) and Google Gemini (Mountain View, CA: Google LLC) on abdominal ultrasound, abdominal CT scan, and abdominal MRI.
Methodology: A cross-sectional original research was conducted over one week in June 2024 to evaluate the quality of patient information brochures produced by ChatGPT 3.
Cureus
November 2024
Department of Orthopedic Surgery, Stony Brook University, Stony Brook, USA.
Background The generation of innovative research ideas is crucial to advancing the field of medicine. As physicians face increasingly demanding clinical schedules, it is important to identify tools that may expedite the research process. Artificial intelligence may offer a promising solution by enabling the efficient generation of novel research ideas.
View Article and Find Full Text PDFMembranes (Basel)
December 2024
NYUAD Water Research Center, New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi 129188, United Arab Emirates.
Membrane engineering is a complex field involving the development of the most suitable membrane process for specific purposes and dealing with the design and operation of membrane technologies. This study analyzed 1424 articles on reverse osmosis (RO) membrane engineering from the Scopus database to provide guidance for future studies. The results show that since the first article was published in 1964, the domain has gained popularity, especially since 2009.
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