Éric Dorveaux

French Version of the User Mobile Application Rating Scale: Adaptation and Validation Study - JMIR mHealth and uHealth Exciting advancements in mHealth app evaluation! Our study successfully translated and validated the French version of the Mobile Application Rating Scale (uMARS-F), addressing a significant gap in assessing mHealth applications in French-speaking regions. With excellent reliability scores across objective and subjective quality, this tool enhances our ability to monitor non-communicable diseases effectively. As we embrace the growing role of e-health technologies, the uMARS-F will empower healthcare professionals to make informed decisions in app selection and regulation. #HealthcareIT #mHealth #DigitalHealth #eHealth #PatientEngagement #HealthTech #Innovation ai.mediformatica.com #health #mhealth #application #healthcare #scale #applications #mobile #study #management #mobilehealth #monitoring #regulation #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/4eWX810)

👉🏼 Diagnosis in Bytes: Comparing the Diagnostic Accuracy of Google and ChatGPT 3.5 as an Educational Support Tool 🤓 Guilherme R Guimaraes 👇🏻 https://lnkd.in/eYfRGsCG 🔍 Focus on data insights: - Google's accuracy for commonly encountered urological conditions was 53.3%, with an additional 23.3% falling within a plausible range of differential diagnoses. - ChatGPT 3.5 outperformed Google with an accuracy of 86.6% in diagnosing prevalent conditions. - ChatGPT 3.5 identified the proper diagnosis in 16.6% of rare cases and offered a reasonable differential diagnosis in half of the cases of unusual disorders. 💡 Main outcomes and implications: - ChatGPT 3.5 demonstrated higher diagnostic accuracy than Google in both contexts. - ChatGPT 3.5 showed satisfactory accuracy when diagnosing common cases, but its performance in identifying rare conditions remains limited. 📚 Field significance: - Evaluating and comparing the diagnostic accuracy of digital tools like Google and ChatGPT 3.5 provides valuable insights into their potential as educational support tools in healthcare. - Understanding the strengths and limitations of these tools can aid in improving diagnostic processes and patient care. 🗄️: [#urology #diagnosticaccuracy #digitaltools #healthcare]

👉🏼 Artificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model 🤓 Vangelis G Alexiou 👇🏻 https://lnkd.in/ewt3NCGV 🔍 Focus on data insights: - 📊 The AI model correctly answered over 65% of the 385 clinical questions assessed. - 📈 Performance varied across 13 vascular surgery topics, but no statistically significant differences were found. - ❌ Analysis revealed that errors stemmed largely from the AI's challenges with complex clinical scenarios and context comprehension. 💡 Main outcomes and implications: - 🧠 The GPT-4 model shows promise in providing clinically relevant answers but requires careful validation for accuracy. - ⚖️ Recognizing AI as a supportive tool rather than a standalone solution is vital for effective clinical application. - 🔍 Insight into specific areas of failure can guide future improvements in AI training and deployment in medical settings. 📚 Field significance: - 🚀 This study lays groundwork for further exploration of AI applications in vascular surgery and potentially other medical fields. - 🤝 Collaboration between AI developers and clinicians can enhance the practical utility of AI tools in healthcare. - 📚 The findings emphasize the importance of integrating technology with human expertise in patient care. 🗄️: [#ArtificialIntelligence #VascularSurgery #MachineLearning #ClinicalDiagnosis #HealthTech #AIinMedicine #NLP #ClinicalValidation #PatientCare #MedicalErrorAnalysis]

👉🏼 Artificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model 🤓 Vangelis G Alexiou 👇🏻 https://lnkd.in/eUnUicWc 🔍 Focus on data insights: - 📊 The AI model correctly answered over 65% of questions across 57 clinical scenarios, demonstrating its capability in vascular surgery context. - 🔍 Performance showed no statistically significant variation across 13 vascular surgery topics, indicating consistency in its application. - ⚠️ A critical review revealed that 27% of its failures were due to a lack of understanding complex clinical scenarios, highlighting areas for improvement. - ❌ The model quoted incorrect or outdated information in 14% of instances, which calls for careful screening of AI-generated responses. - 🧠 Inability to comprehend nuances and medical classification systems accounted for 11% of errors, showcasing limitations in contextual understanding. 💡 Main outcomes and implications: - 🤖 GPT-4 has potential as a clinical decision support tool but necessitates validation of reasoning and clinical accuracy. - 🔑 Recognition of AI as a supportive mechanism rather than a standalone diagnostic solution is crucial, ensuring human oversight in patient management. - 📈 The findings may guide further development of AI systems in medical fields, emphasizing enhanced training on complex clinical scenarios. - 🌐 Broadens the scope for integrating AI technology into healthcare practices, pushing towards more efficient healthcare delivery while acknowledging current limitations. 📚 Field significance: - 🚀 Represents a significant step in AI's integration into medicine, particularly in vascular surgery diagnostics and management. - 🌍 Highlights the growing importance of AI in improving clinical outcomes and operational efficiencies in healthcare settings. - 🔄 Encourages ongoing research into the adaptability and reliability of AI models, aiming for safer and more effective healthcare solutions. - 💬 Sparks discussions regarding ethical implications and the role of AI in augmenting healthcare professionals' capabilities without replacing them. 🗄️: [#ArtificialIntelligence #VascularSurgery #GPT4 #MachineLearning #ClinicalDecisionSupport #NaturalLanguageProcessing #HealthcareInnovation #DiagnosticTools]

New on the blog: Healthcare AI is Not Hype : Empowering Consumers, Enabling Providers and Advancing The Science”™ -“AI Globally is a $19T Opportunity” "Recently, Axios reported that AI could add $19.9 trillion to the global economy by 2030, driven in part by its effects on consumer spending and the infrastructure required for AI systems. The future is indeed promising, especially for healthcare, though not necessarily in the ways people expect." Read more: https://lnkd.in/eJesEeQF

New on the blog: Healthcare AI is Not Hype : Empowering Consumers, Enabling Providers and Advancing The Science”™ -“AI Globally is a $19T Opportunity” "Recently, Axios reported that AI could add $19.9 trillion to the global economy by 2030, driven in part by its effects on consumer spending and the infrastructure required for AI systems. The future is indeed promising, especially for healthcare, though not necessarily in the ways people expect." Read more: https://lnkd.in/eJesEeQF

New on the blog: Healthcare AI is Not Hype : Empowering Consumers, Enabling Providers and Advancing The Science”™ -“AI Globally is a $19T Opportunity” "Recently, Axios reported that AI could add $19.9 trillion to the global economy by 2030, driven in part by its effects on consumer spending and the infrastructure required for AI systems. The future is indeed promising, especially for healthcare, though not necessarily in the ways people expect." Read more: https://lnkd.in/eJesEeQF

New on the blog: Healthcare AI is Not Hype : Empowering Consumers, Enabling Providers and Advancing The Science”™ -“AI Globally is a $19T Opportunity” "Recently, Axios reported that AI could add $19.9 trillion to the global economy by 2030, driven in part by its effects on consumer spending and the infrastructure required for AI systems. The future is indeed promising, especially for healthcare, though not necessarily in the ways people expect." Read more: https://lnkd.in/eJesEeQF

Today the most important article in chatgpt and "ChatGPT and Vaccine Hesitancy: A Comparison of English, Spanish, and French Responses Using a Validated Scale" - ChatGPT is a widely used information system with over 1 billion visits in August 2023. - The study focuses on assessing the quality and equity of ChatGPT responses regarding health-related topics like vaccination. - Utilizes the Vaccine Hesitancy Scale (VHS) to measure hesitancy in ChatGPT responses across English, Spanish, and French languages. - Findings reveal that ChatGPT responses show lower hesitancy compared to human respondents in previous studies. - Significant variations exist in ChatGPT responses among different languages, with English responses being the most hesitant and Spanish the least. - Consistency observed in ChatGPT responses across various model parameters but some variations based on scale factors such as vaccine competency and risk. - Implications for researchers interested in evaluating and enhancing the quality