Nick Tarazona, MD’s Post

👉🏼 Evaluating ChatGPT-3.5 and ChatGPT-4.0 Responses on Hyperlipidemia for Patient Education 🤓 Thomas J Lee 👇🏻 https://lnkd.in/gkMCz67y 🔍 Focus on data insights: - ChatGPT version 4.0 had a significantly higher grade reading level than version 3.5 (p = 0.0002). - ChatGPT version 4.0 had a significantly lower word count than version 3.5 (p = 0.0073). - Both versions provided accurate but sometimes partially complete responses. 💡 Main outcomes and implications: - No significant difference in accuracy between the free and paid versions of hyperlipidemia FAQs. - Version 4.0 offered more concise and readable information, aligning with the readability of most online medical resources. - The paid version demonstrated superior adaptability in tailoring responses based on the input. 📚 Field significance: - ChatGPT can be recommended as a reliable source of patient education, regardless of the version used. - Future research should explore diverse question formulations and ChatGPT's handling of incorrect information. 🗄️: [#hyperlipidemia #patienteducation #ChatGPT #datainsights]

👉🏼 ChatGPT in medical libraries, possibilities and future directions: An integrative review 🤓 Author: Brady D Lund 👇🏻 🔍 Focus on data insights: - Comprehensive analysis of existing literature on ChatGPT and its potential implementations within library contexts. - Systematic literature search across various databases yielded 166 papers, with 30 excluded for irrelevance. - Critical Appraisal Skills Programme qualitative checklist further narrowed down to 29 papers. 💡 Main outcomes and implications: - Diverse applications of ChatGPT in medical libraries, including aiding users in finding relevant medical information, answering queries, providing recommendations, and facilitating access to resources. - Potential challenges and ethical considerations associated with ChatGPT in this context are highlighted. 📚 Field significance: - The integration of ChatGPT into medical library services holds promise for enhancing information retrieval and user experience, benefiting library users and the broader medical community. 🔗 Source: [Read more](https://lnkd.in/epqYsEj7) 🗄️ Keywords: [#ChatGPT #medical libraries #data insights #applications #information retrieval]

👉🏼 Using Google web search to analyze and evaluate the application of ChatGPT in femoroacetabular impingement syndrome 🤓 Yifan Chen 👇🏻 https://lnkd.in/eKgnW-xU 🔍 Focus on data insights: - 40% of the questions analyzed were similar between Google and ChatGPT. - Notable differences in answers for 60% of the top 5 most common questions. - Expert evaluation showed high satisfaction with ChatGPT's descriptions of treatment options and safety information. 💡 Main outcomes and implications: - ChatGPT shows potential as a supplementary health information resource for FAI. - Experts find ChatGPT capable of providing accurate and comprehensive responses. - Continuous improvements in medical content depth and precision are recommended for reliability. 📚 Field significance: - ChatGPT could serve as a reliable medical resource for initial information retrieval. - Validation is crucial before fully embracing ChatGPT as a trusted medical resource. 🗄️: [#datainsights #healthinformation #ChatGPT #FAI]

Are medical studies being written with ChatGPT? Well, we all know ChatGPT overuses the word "delve". Look below at how often the word 'delve' is used in papers on PubMed (2023 was the first full year of ChatGPT). PS: Not just chatGPT but Gemini too overuses the word "delve"😶 Original Post on X: https://lnkd.in/dfabdbjT #artificialintelligence #chatgpt #researchers #research #publications #pubmed

👉🏼 Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical Abstracts 🤓 Joel Hake 👇🏻 https://lnkd.in/gTpB2BCB 🔍 Focus on data insights: - ChatGPT produced summaries that were 70% shorter than the mean abstract length. - Summaries were rated as high quality (median score 90) and high accuracy (median 92.5). - Bias in the ChatGPT summaries was low, with serious inaccuracies and hallucinations being uncommon. 💡 Main outcomes and implications: - ChatGPT can help family physicians accelerate the review of scientific literature. - The software pyJournalWatch has been developed to support this application. - Life-critical medical decisions should still be based on a full evaluation of research articles in context with clinical guidelines. 📚 Field significance: - Enhancing the efficiency of medical literature review. - Potential for improving access to relevant information for medical professionals. 🗄️: #datainsights #medicalresearch #ChatGPT #literaturesummarization

Informed consent documents in medicine and clinical research are often complex and technical. Using #chatgpt can make them more understandable and accessible. A nice article out this week from Fatima N. Mirza, James Zou et al , showcases the use of ChatGPT in facilitating "truly informed medical consent". The research team used ChatGPT to transform original surgical consent forms from a >12th-grade reading level to a Flesch-Kincaid reading level of 6.7. A few key highlights: 📄 Text Simplification plays into LLM's key strengths; making ChatGPT well-suited for these types of tasks. ⏱️: This same task might take humans hours or days; it can be done by ChatGPT in less than a minute. 💡: Utilizing LLM/ChatGPT to modify existing source material rather than generating new text helps reduce risks such as biases or inaccuracies. Note: If you want to try this yourself, the #aiprompt used in this particular study was: "While preserving content and meaning, convert this consent to the average American reading level'." #clinicalresearch #accessibility #informedconsent h/t Julia Moore Vogel

👉🏼 Can ChatGPT pass the MRCP (UK) written examinations? Analysis of performance and errors using a clinical decision-reasoning framework 🤓 Amy Maitland 👇🏻 https://lnkd.in/eUcFHVtR 🔍 Focus on data insights: - ChatGPT achieved accuracy rates of 86.3% (part 1) and 70.3% (part 2). - Weak but significant correlations were found between ChatGPT's accuracy and just-passing rates in part 2 (r=0.34, p=0.0001) and question length in part 1 (r=-0.19, p=0.008). - Eight types of errors were identified, with the most frequent being factual errors, context errors, and omission errors. 💡 Main outcomes and implications: - ChatGPT performance exceeded the passing mark for both exams. - Multiple choice examinations provide a benchmark for LLM performance comparable to human demonstrations of knowledge. - Understanding the reasons behind ChatGPT's errors can help develop strategies to prevent them in medical devices incorporating LLM technology. 📚 Field significance: - Analysis of ChatGPT's performance on MRCP examinations sheds light on the potential role of large language models in healthcare education and decision-making. - Identifying error patterns can guide improvements in LLM technology for medical applications. 🗄️: [#largeLanguageModels #healthcare #dataInsights #clinicalDecisionMaking]

👉🏼 Utilizing ChatGPT as a scientific reasoning engine to differentiate conflicting evidence and summarize challenges in controversial clinical questions 🤓 Shiyao Xie 👇🏻 https://lnkd.in/e48btBf4 🔍 Focus on data insights: - The gpt-4-1106-preview model achieved a 90% recall rate in detecting inconsistent claim pairs within a ternary assertions setup. - ChatGPT provided sound reasoning for the assertions between claims and hypotheses, based on an analysis grounded in relevance, specificity, and certainty. 💡 Main outcomes and implications: - ChatGPT's conclusions of consensus and controversies in clinical literature were comprehensive and factually consistent. - The research questions proposed by ChatGPT received high expert ratings. 📚 Field significance: - ChatGPT's capacity to evaluate and interpret scientific claims can be generalized to broader clinical research literature. - Caution is advised as ChatGPT's outputs are inferences drawn from the input literature and could be harmful to clinical practice. 🗄️: [#clinicalresearch, #scientificreasoning, #ChatGPT, #datainsights]

Today the most important article in chatgpt and "Enhancing readability of USFDA patient communications through large language models: a proof-of-concept study" - Large language models (LLMs) significantly improved the readability of USFDA patient communications, reducing grade levels to more accessible readings. - The study showed that LLM outputs retained technical accuracy and key messages while simplifying complex health-related information. - Future research is recommended to extend these findings to other languages and patient groups in real-world settings. #healthcare #patientcommunication #languagemodels #readability #research

👉🏼 A comparative analysis of ChatGPT, ChatGPT-4 and Google Bard performances at the Advanced Burn Life Support Exam 🤓 Mario Alessandri-Bonetti 👇🏻 https://lnkd.in/eEqGmtE2 🔍 Focus on data insights: - ChatGPT-4 scored 90% (45 out of 50) on the ABLS exam. - ChatGPT-4 outperformed Google Bard significantly with a p-value of 0.012. 💡 Main outcomes and implications: - LLMs like ChatGPT-4 show high accuracy in medical exams. - Potential applications of LLMs in emergency care require further monitoring. 📚 Field significance: - Integration of LLMs in medical education and clinical decision-making. - Importance of complementing human cognition with AI tools. 🗄️: #ArtificialIntelligence #Healthcare #ClinicalDecisionMaking #MedicalEducation #BurnCare