Peter Kecskemethy

AI's Accuracy and Readability in Patient Aid for Third Molar Extractions #AI #PatientAid #Accuracy #Readability Link: https://lnkd.in/e2Fg2BhQ

👉🏼 Evaluating Large Language Models for Automated Reporting and Data Systems Categorization: Cross-Sectional Study 🤓 Qingxia Wu 👇🏻 https://lnkd.in/ey5PBKef 🔍 Focus on data insights: - Large language models show promise for improving radiology workflows. - Performance on structured radiological tasks like Reporting and Data Systems (RADS) categorization is crucial. - Evaluation of chatbots' ability to assign RADS categories to radiology reports. 💡 Main outcomes and implications: - Claude-2 demonstrated potential in assigning RADS categories based on established criteria like LI-RADS version 2018. - Chatbots' performance improved with structured prompts and guideline PDFs. - Current chatbot generation struggles with accurately categorizing cases based on recent RADS criteria. 📚 Field significance: - Advancements in large language models can enhance radiology workflows. - Structured prompts and guideline PDFs play a vital role in improving chatbots' performance. - Continued research needed to align chatbot capabilities with evolving RADS criteria. 🗄️: #radiology #chatbots #RADS #datainsights #largeLanguageModels

👉🏼 Assessment of the Quality and Readability of Information Provided by ChatGPT in Relation to the Use of Platelet-Rich Plasma Therapy for Osteoarthritis 🤓 Stephen Fahy 👇🏻 https://lnkd.in/eeJqGdyR 🔍 Focus on data insights: - ChatGPT versions 3.5 and 4 produced moderate quality information. - Quality of information provided by ChatGPT version 4 was significantly better than version 3.5. - Both models emphasized the importance of shared decision making. 💡 Main outcomes and implications: - Content generated exceeded recommended reading levels for patient education materials (PEMs). - Readability remains a significant barrier to widespread usage. - Future iterations should focus on producing more accessible content for patient education. 📚 Field significance: - Collaboration between healthcare providers, patient organizations, and AI developers is crucial. - High-quality, easily understandable information is essential for informed healthcare decisions. 🗄️: #datainsights #patienteducation #PRPtherapy #healthcaredecisions

👉🏼 Evaluating MediBetter: A Mobile Application for Health Monitoring and Medication Management 🤓 Bian Yang 👇🏻 https://lnkd.in/epqPGBvH 🔍 Focus on data insights: - ChatGPT-generated summaries outperformed user-generated ones in terms of informativeness, coherence, fluency, consistency, and contradiction handling. - Participants found the app's features highly useful for health monitoring and medication adherence. 💡 Main outcomes and implications: - MediBetter enhances health monitoring and medication adherence through its innovative features. - AI-based summarization improves the quality of health reports and enhances user experience. 📚 Field significance: - Advances in mobile health technology are crucial for empowering patients in managing their health effectively. - Integration of AI in health applications can significantly improve patient outcomes and engagement. 🗄️: [#healthtechnology #mobileapp #AIintegration #patientempowerment]

👉🏼 Evaluation of ChatGPT's Potential in Tailoring Gynecological Cancer Therapies 🤓 Annika Krückel 👇🏻 https://lnkd.in/eYjbGzMf 🔍 Focus on data insights: - ChatGPT demonstrated a good potential in generating therapy recommendations with an average score of 0.75 points for patients with ovarian cancer, 0.7 points for cervical, and 1.5 points for endometrial cancer patients. - Individual patient characteristics were regularly considered by ChatGPT. - ChatGPT reliably indicated aftercare and provided detailed information on preventive measures as well as supportive treatment. 💡 Main outcomes and implications: - ChatGPT is a promising tool for the generation of therapy suggestions for gynecological carcinomas with high flexibility in response to individual patient differences. - At the current state, however, ChatGPT is not suitable for replacing expert panels. 📚 Field significance: - Healthcare system efficiency - Patient care enhancement - Therapy recommendation personalization 🗄️: [#gynecologicalcancer #therapyrecommendations #ChatGPT #datainsights]

👉🏼 AI in the repurposing of potential herbs for filariasis therapy 🤓 Somsri Wiwanitmkit 👇🏻 https://lnkd.in/edD44fmh 🔍 Focus on data insights: - ChatGPT provided appropriate suggestions for potential medication repurposing in all ten clinical scenarios. - The drug recommendations aligned with current medical research and literature. 💡 Main outcomes and implications: - ChatGPT offers promise as a valuable tool for repurposing drugs in filariasis treatment. - Its concise responses can aid in identifying potential pharmacological candidates. 📚 Field significance: - Further research and development are needed to optimize ChatGPT's use in filariasis therapy settings. 🗄️: #AI #filariasis #herbs #therapy

👉🏼 Assessing the utility of artificial intelligence throughout the triage outpatients: a prospective randomized controlled clinical study 🤓 Xiaoni Liu 👇🏻 https://lnkd.in/ePUT-sr9 🔍 Focus on data insights: - ChatGPT demonstrated high consistency with manual triage in outpatient settings. - Expert ratings revealed ChatGPT's responses to be professional, comprehensive, and humanized. 💡 Main outcomes and implications: - ChatGPT can help patients receive timely treatment, improve diagnosis and cure rates, and alleviate medical staff shortages. 📚 Field significance: - Integration of artificial intelligence tools like ChatGPT can enhance outpatient triage efficiency and accuracy. 🗄️: [#artificialintelligence #outpatienttriage #clinicalstudy]

👉🏼 The performance of large language model powered chatbots compared to oncology physicians on colorectal cancer queries 🤓 Shan Zhou 👇🏻 https://lnkd.in/gQ9hfQq3 🔍 Focus on data insights: - Large language model (LLM)-powered chatbots outperformed oncology physicians in accuracy. - Claude 2.1 demonstrated the highest accuracy among the chatbots. - ChatGPT-3.5 had the lowest accuracy among the chatbots. 💡 Main outcomes and implications: - LLM-powered chatbots can provide more accurate medical information than oncology physicians. - Chatbots like Claude 2.1 show potential for improving healthcare information dissemination. - Integration of LLM-powered chatbots in healthcare settings could enhance access to accurate medical information. 📚 Field significance: - Advancements in artificial intelligence, particularly in chatbot technology, are reshaping healthcare information delivery. - Data-driven insights from this study highlight the potential of LLM-powered chatbots in improving medical query responses. - Implications for future research in leveraging AI technologies for enhancing healthcare communication. 🗄️: [#large #language #model #chatbots #oncology #physicians #colorectal #cancer #accuracy #healthcare #AI #technology]

👉🏼 An example of leveraging AI for documentation: ChatGPT-generated nursing care plan for an older adult with lung cancer 🤓 Fabiana C Dos Santos 👇🏻 https://lnkd.in/er97YBrb 🔍 Focus on data insights: - The study demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt for generating nursing care plan suggestions. - Methodology for creating consistent care plan suggestions using ChatGPT is outlined. - Assessment of the quality of care plans produced by ChatGPT is discussed. 💡 Main outcomes and implications: - Not all suggested care plan terms utilized standardized nursing terminology, but the ChatGPT-generated care plan closely matched the gold standard in scope and nature. - Correct prioritization of oxygenation and ventilation needs in the care plan. - Potential value of ChatGPT as a decision support tool for optimizing cancer care documentation. 📚 Field significance: - Demonstrates the potential of AI in generating nursing care plans. - Highlights the importance of utilizing AI for decision support in cancer care documentation. 🗄️: [#AI #documentation #nursingcare #cancer #ChatGPT]

👉🏼 ChatGPT can help guide and empower patients after prostate cancer diagnosis 🤓 Harry Collin 👇🏻 https://lnkd.in/ewcGRfZY 🔍 Focus on data insights: - ChatGPT generates broad and representative questions related to prostate cancer. - The AI platform provides understandable and clinically sound advice to newly diagnosed patients. 💡 Main outcomes and implications: - AI can play a crucial role in educating and empowering patients post-prostate cancer diagnosis. - ChatGPT's responses show potential for guiding patients effectively in their healthcare journey. 📚 Field significance: - Integrating AI like ChatGPT can enhance patient education and support in the healthcare sector. - Further evaluation and optimization of ChatGPT in the healthcare field are necessary for maximizing its benefits. 🗄️: [#prostatecancer #AI #patienteducation #healthcare]

👉🏼 Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange 🤓 Dukyong Yoon 👇🏻 https://lnkd.in/eeJTXmMa 🔍 Focus on data insights: - Advanced language models, such as Large Language Models (LLMs), can significantly enhance health care data interoperability. - LLMs demonstrate high accuracy and efficiency in transforming and exchanging medical data. - The study showcases the potential of LLMs in overcoming challenges related to nonstandardized or unstructured medical records. 💡 Main outcomes and implications: - LLMs show a high conversion accuracy in transforming laboratory results and diagnostic codes. - LLMs offer enhanced consistency in diagnostic code conversion compared to traditional mapping approaches. - LLMs exhibit a positive predictive value in extracting specific information from unstructured clinical records. 📚 Field significance: - The findings suggest that LLMs have the potential to revolutionize health care data exchange by streamlining information transfer and improving interoperability. - LLMs could reduce the complexity associated with standardizing medical terms and data structures, leading to more efficient data sharing in the healthcare industry. 🗄️: #HealthCare #DataInteroperability #LanguageModels #MedicalDataExchange

👉🏼 Systematic review: The use of large language models as medical chatbots in digestive diseases 🤓 Mauro Giuffrè 👇🏻 https://lnkd.in/ea2Q9ZZS 🔍 Focus on data insights: - Current general-purpose LLMs have unacceptably low accuracy on clinical gastroenterology and hepatology tasks - Accuracy of question-responding varied across different model versions - Absence of standardized methodology and reporting metrics for studies involving LLMs 💡 Main outcomes and implications: - Low accuracy may lead to adverse patient safety events - Incorrect information or triage recommendations could overburden healthcare systems - Delay necessary care for patients 📚 Field significance: - Highlight the need for improved accuracy in LLMs for medical applications - Emphasize the importance of standardized methodologies and reporting metrics in LLM studies 🗄️: [#digestivediseases #medicalchatbots #LLMs #accuracy #healthcaresystems]

👉🏼 Exploring the Efficacy of Large Language Models in Enhancing English Academic Writing Proficiency for Non-Native Medical Students: A Study on ChatGPT 🤓 Jiakun Li 👇🏻 https://lnkd.in/eW_GbX6g 🔍 Focus on data insights: - Implementation of ChatGPT led to a significant increase in manual scoring by 4.23 points. - AI scoring based on ChatGPT-3.5 and ChatGPT-4 models showed improvements of 4.82 and 3.84 points, respectively. 💡 Main outcomes and implications: - Large language models like ChatGPT show promise in enhancing academic writing skills for non-native English speakers. - ChatGPT can potentially assist teachers in the grading process, as indicated by the statistical analysis showing no significant difference between manual scoring and ChatGPT-4 scoring. - Positive student feedback highlighted improvements in writing quality (92%), language skills (84%), and support for academic research (76%). 📚 Field significance: - Augmenting English academic writing proficiency for non-native speakers in medical education. - Contribution of large language models to the educational evaluation process in non-English primary language environments. 🗄️: [#academicwriting #ChatGPT #Englishproficiency #medicalstudents]