AI meets Rheumatology: ChatGPT and patient response optimization Large language models like ChatGPT, trained on vast text data, are revolutionizing healthcare by understanding and generating human-like language.
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AI meets Rheumatology: ChatGPT and patient response optimization Large language models like ChatGPT, trained on vast text data, are revolutionizing healthcare by understanding and generating human-like language.
AI meets Rheumatology: ChatGPT and patient response optimization | RheumNow
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Many consumers and medical providers are turning to chatbots, powered by large language models to answer medical questions and inform treatment choices. Five major large language models was subjected to parts of the U.S. Medical Licensing Examination Step 3 examination, widely regarded as the most challenging. Here’s how ChatGPT, Claude, Google Gemini, Grok and Llama performed. ChatGPT-4o (OpenAI) — 49/50 questions correct (98%) Claude 3.5 (Anthropic) — 45/50 (90%) Gemini Advanced (Google) — 43/50 (86%) Grok (xAI) — 42/50 (84%) HuggingChat (Llama) — 33/50 (66%) #AI #LLM #healthcare #doctors Scott Gottlieb https://lnkd.in/dqAvmN-9
Op-ed: How well can AI chatbots mimic doctors in a treatment setting? We put 5 to the test
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🚨 AI can't replace your doctor! 🚨 Ever thought about turning to ChatGPT for medical advice? Think again. 🤔 A new study from Western University says it's a BIG NO. 👎 🌟 The AI world is advancing rapidly, but when it comes to your health, always consult the experts. Discover why AI isn't your go-to for medical advice here: [full article] 👇 https://lnkd.in/g7yhxQSH 🚀 Let's build smarter, but remember: There's no replacement for professional healthcare! 💡 Share your thoughts or experiences in the comments! 🗣️👇
Should you turn to ChatGPT for medical advice? No, Western University study says - CBC
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👉🏼 Comparative performance analysis of large language models: ChatGPT-3.5, ChatGPT-4 and Google Gemini in glucocorticoid-induced osteoporosis 🤓 Linjian Tong 👇🏻 https://lnkd.in/gNEKXZh8 🔍 Focus on data insights: - 📊 Google Gemini offered more concise responses compared to its counterparts, enhancing clarity in medical information. - 🚀 ChatGPT-4 outperformed ChatGPT-3.5 significantly in total scores related to pathogenesis queries, indicating improvements in understanding complex medical topics. - 🔄 Both ChatGPT-3.5 and ChatGPT-4 demonstrated better self-correction abilities, suggesting advancements in learning from previous responses. 💡 Main outcomes and implications: - 🌟 The study highlights the potential for large language models to support clinical decision-making through effective information management. - 🏥 ChatGPT-4's superior performance indicates its utility for healthcare professionals, particularly in answering guideline-related questions. - 💬 The differences in performance among the models suggest that selection of language models in healthcare settings can impact the quality of responses received, emphasizing the need for careful choice based on model strengths. 📚 Field significance: - 🧠 The findings underscore the relevance of artificial intelligence in improving medical practice, particularly in areas requiring extensive knowledge and rapid information retrieval. - 📈 Advancements in language model capabilities can lead to enhanced patient care through improved access to accurate health information. - 🔍 Ongoing evaluation of these models is critical to ensure they meet the evolving needs of the medical community. 🗄️: [#large_language_models #glucocorticoid_induced_osteoporosis #AI_in_medicine #ChatGPT #Google_Gemini #clinical_decision_making]
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In 2022, we saw ChatGPT take off, bringing with it a wave of new AI technology – including LLMs. From increased efficiencies to better communication, people around the world were experiencing their first taste of the future. This especially goes for the healthcare industry. LLMs alone have been improving patient care through an enhanced system of accessibility and communication which serves to help avoid jargon or misinformation. However, as Piotr Orzechowski points out, there's still a chance of bias, which must be comprehensively addressed before these systems are fully activated. #PatientEngagement #LLM #AI
How Large Language Models Will Improve the Patient Experience - MedCity News
https://medcitynews.com
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👍 Many consumers and medical providers are turning to chatbots, powered by large language models, to answer medical questions and inform treatment choices. Here's how ChatGPT, Claude, Google Gemini, Grok and Llama performed. Transformation wirh Technology 👌
Op-ed: How well can AI chatbots mimic doctors in a treatment setting? We put 5 to the test
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One in ten doctors use ChatGPT for everyday tasks and some patients are turning to AI for self-diagnosis. But a 2024 study highlighted significant challenges with LLMs’ reliability in healthcare. Read one of our top posts this year on the Stanford HAI blog: https://lnkd.in/gjm4xgQr
Generating Medical Errors: GenAI and Erroneous Medical References
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JIM│ Large language models in critical care Author: Paul Elbers et al. @Paul Elbers Link: https://buff.ly/4hnbwR9 #LargeLanguageModels #IntensiveCareMedicine #CriticalCareMedicine #NaturalLanguageProcessing #ArtificialIntelligence #MachineLearning Large language models (LLMs), like ChatGPT, are advanced AI tools capable of understanding and generating text in a way that mimics human communication. In critical care medicine, where healthcare providers manage complex situations and large amounts of information, these models have the potential to make a significant impact. For example, they could reduce the paperwork burden by automating tasks like writing patient notes or summarizing medical charts, giving doctors and nurses more time to focus on patient care. They might also support medical teams by analyzing data to assist with diagnosing conditions or planning treatments. Additionally, LLMs could improve communication by translating complex medical information into simpler terms, helping patients and their families better understand their conditions and care plans. They also show promise in extracting useful insights from unstructured or incomplete medical records, potentially improving the overall quality of healthcare data. However, challenges remain. LLMs are not always reliable and can produce incorrect or biased information, which could lead to mistakes in clinical decision-making. Ethical concerns, such as ensuring patient privacy and fairness, also need to be addressed. To safely and effectively use these tools, healthcare professionals will require proper training to understand their capabilities and limitations. Looking ahead, combining LLMs with other AI technologies may make them more reliable and useful in practice. These tools must undergo rigorous testing and meet strict safety standards to ensure they enhance, rather than hinder, patient care. With responsible implementation and appropriate training, LLMs have the potential to transform critical care medicine, making it more efficient and focused on the needs of patients.
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🔬 A new study in BMJ Open comparing ChatGPT (GPT-4) vs. human doctors on complex Swedish family medicine specialist exam cases! 𝗞𝗲𝘆 𝗳𝗶𝗻𝗱𝗶𝗻𝗴𝘀: 📊 𝗢𝗻 𝗮 𝟭𝟬-𝗽𝗼𝗶𝗻𝘁 𝘀𝗰𝗮𝗹𝗲: - Random doctor responses: 6.0 - Top-tier doctor responses: 7.2 - GPT-4 responses: 4.5 - Updated GPT-4o: Showed improvement but still lagged behind human doctors 💡 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀: - Human doctors significantly outperformed GPT-4, especially in crucial areas like diagnosis, lab tests, physical examinations, and legal matters - GPT-4 was less efficient at conveying relevant information concisely - The gap between AI and human doctors remains meaningful in complex medical cases 🌍 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘇𝗮𝗯𝗶𝗹𝗶𝘁𝘆: - Study was conducted in Swedish primary care setting - Results may vary across different healthcare systems and countries - Core findings about AI's limitations in complex medical decision-making likely apply broadly - More research needed in diverse healthcare contexts 🏥 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: - GPT-4 should not be used directly for medical decision support in primary care - While AI shows promise, human oversight remains essential - Future chatbots need significant improvements before clinical implementation 🎯 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: While AI continues to advance rapidly, complex medical decision-making still requires human expertise. The study highlights both the potential and current limitations of AI in healthcare, emphasizing the need for careful evaluation before implementing these tools in clinical practice. Arvidsson R, Gunnarsson R, Entezarjou A, et al. ChatGPT (GPT-4) versus doctors on complex cases of the Swedish family medicine specialist examination: an observational comparative study. BMJ Open 2024;14:e086148. doi:10.1136/ bmjopen-2024-086148 #HealthcareAI #MedicalEducation #ArtificialIntelligence #HealthTech #FutureMedicine #MedicalResearch
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👉🏼 Accuracy of ChatGPT3.5 in answering clinical questions on guidelines for severe acute pancreatitis 🤓 Jun Qiu 👇🏻 https://lnkd.in/e-ZDXrnE 🔍 Focus on data insights: - 📊 ChatGPT3.5 demonstrated a higher accuracy rate in English (71%) compared to Chinese (59%). - 📝 The model was more effective in answering short-answer questions (76%) than true/false questions (60%). - 🔍 Statistical analysis showed no significant difference in accuracy between languages and question types (P value: 0.203 for language, 0.405 for question type). 💡 Main outcomes and implications: - ⚖️ The findings suggest that while ChatGPT3.5 can assist clinicians, it should not be the sole resource for clinical decision-making. - 🧠 The study highlights the importance of language proficiency in AI-assisted medical inquiries. - 🔄 There is a need for further research to enhance the reliability of AI tools in clinical settings. 📚 Field significance: - 🌐 This research contributes to the growing body of literature on AI applications in healthcare, particularly in interpreting clinical guidelines. - 🏥 It underscores the necessity for medical professionals to critically evaluate AI-generated information. - 📈 The results may influence future developments in AI training and deployment in medical contexts. 🗄️: [#AI #ChatGPT #ClinicalGuidelines #SevereAcutePancreatitis #MedicalDecisionMaking #HealthcareAI #DataInsights]
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