Stefan Niculescu

👉🏼 ChatGPT's Performance in Clinical Decision-Making: A Comparative Study of NASS Guidelines and AI Responses 🤓 Ankur Kayastha 👇🏻 https://lnkd.in/ebFtSS7G 🔍 Focus on data insights: - 📊 ChatGPT-4.0 demonstrated a 67% accuracy rate compared to 47% for ChatGPT-3.5, indicating improved performance in clinical queries. - 📉 The study revealed that ChatGPT-3.5 provided 100% supplementary information, while ChatGPT-4.0 reduced this to 67%, suggesting a more concise response style. - 🔍 Diagnostic testing accuracy improved significantly from 0% in ChatGPT-3.5 to 100% in ChatGPT-4.0, highlighting advancements in AI capabilities. 💡 Main outcomes and implications: - ⚖️ The findings suggest that while both versions of ChatGPT show reasonable concordance with established NASS guidelines, there is a need for caution in clinical applications due to potential misinformation. - 🧠 The study emphasizes the importance of continuous improvement in AI tools for clinical decision-making, as higher accuracy can lead to better patient outcomes. - 🔄 The results advocate for further research into AI's role in healthcare, particularly in enhancing diagnostic and treatment recommendations. 📚 Field significance: - 🌐 The integration of AI in clinical settings could revolutionize how healthcare professionals access and utilize medical guidelines. - 🏥 Improved AI performance may lead to more efficient decision-making processes, ultimately benefiting patient care and resource management. - 🔬 This study contributes to the growing body of literature on AI's effectiveness in medicine, paving the way for future innovations and applications. 🗄️: [#AI #ChatGPT #ClinicalGuidelines #NASS #Healthcare #DecisionMaking #DiagnosticAccuracy #PatientCare #ArtificialIntelligence]

👉🏼 Large Language Models for Efficient Medical Information Extraction 🤓 Navya Bhagat 👇🏻 https://lnkd.in/eH4SKqS2 🔍 Focus on data insights: - ChatGPT demonstrates remarkable capabilities in sensitivity for depression and heavy smokers, as well as specificity for cancer. - Areas for improvement identified in capturing nuanced semantic information related to family history of heart disease and cancer. 💡 Main outcomes and implications: - Large language models like ChatGPT show substantial potential for advancements in medical information extraction. - Enhancing efficiency in treating patients and improving the standard of care in healthcare. 📚 Field significance: - Advancements in utilizing large language models for extracting valuable insights from unstructured clinical narrative reports. - Potential improvements in healthcare worker efficiency and patient care through innovative technology. 🗄️: [#medicalinformationextraction #healthcareefficiency #largelanguagemodels]

👉🏼 Evaluating the Appropriateness, Consistency, and Readability of ChatGPT in Critical Care Recommendations 🤓 Kaan Y Balta 👇🏻 https://lnkd.in/eTiUVmGx 🔍 Focus on data insights: - 📊 ChatGPT 4.0 demonstrated significantly higher appropriateness scores compared to ChatGPT 3.5, indicating improvements in generating relevant clinical recommendations. - 🔄 Both models exhibited variability in consistency, with ChatGPT 4.0 showing a 40% consistency rate versus 28% for ChatGPT 3.5, suggesting room for improvement in reliability. - 📚 Readability levels were similar across both versions, highlighting that while the content may be appropriate, it remains complex and potentially challenging for some users. 💡 Main outcomes and implications: - ⚠️ The presence of "hallucinations" in both models emphasizes the critical need for domain expertise when interpreting AI-generated recommendations in clinical settings. - 🧠 Clinicians must be aware of the strengths and limitations of LLMs to ensure safe and effective use in critical care, as reliance on these tools without proper understanding can lead to misinformation. - 🔍 The study calls for further research into enhancing the consistency and reliability of AI models in medical applications. 📚 Field significance: - 🌐 This research contributes to the ongoing discourse on the integration of AI in healthcare, particularly in critical care, where accurate and reliable information is paramount. - 🏥 Understanding the performance of LLMs like ChatGPT can guide future developments and training of AI systems to better serve clinical needs. - 📈 The findings underscore the importance of continuous evaluation and adaptation of AI tools in medical practice to align with evolving clinical standards. 🗄️: [#ChatGPT #CriticalCare #ArtificialIntelligence #Healthcare #ClinicalRecommendations #DataInsights #MedicalAI #LLM #Misinformation]

👉🏼 Application of ChatGPT to Support Nutritional Recommendations for Dialysis Patients - A Qualitative and Quantitative Evaluation 🤓 Lin-Chun Wang 👇🏻 https://lnkd.in/et9eMT2R 🔍 Focus on data insights: - 📊 ChatGPT-4 generated a daily meal plan that was evaluated for nutritional accuracy. - 🥗 The renal dietitian rated the quality of recipes and cooking instructions, revealing strengths and weaknesses in AI-generated content. - 💻 The study compared ChatGPT’s nutritional analysis to USDA-approved software, highlighting significant underestimations in nutrient values. 💡 Main outcomes and implications: - 🔍 ChatGPT's nutritional analysis exhibited major inaccuracies, notably underestimating calories and essential nutrients by substantial margins. - 📉 This suggests a need for enhanced algorithms or oversight when utilizing AI for medical nutritional advice. - 🌍 The ability to translate nutritional guidance into multiple languages speaks to improving accessibility, although translation reliability was noted. 📚 Field significance: - 🚑 This study emphasizes the critical role of qualitative assessments of AI-generated content in healthcare settings. - 🤖 It underscores the potential of AI tools to support personalized dietary recommendations but stresses the necessity for rigorous evaluation before clinical application. - ⚕️ Findings may influence future research and development in AI-based tools for dietary management, particularly in specialized patient populations like dialysis patients. 🗄️: [#ChatGPT #Dialysis #Nutrition #AI #MedicalApplications #PersonalizedCare #NutritionalAnalysis #HealthTech]

👉🏼 Appropriateness of ChatGPT in Answering Heart Failure Related Questions 🤓 Ryan C King 👇🏻 https://lnkd.in/e2e82tuB 🔍 Focus on data insights: - GPT-4 provided 100% correct responses to heart failure-related questions. - GPT-4 displayed a higher proportion of comprehensive knowledge compared to GPT-3.5. - Both models showed high reproducibility in their responses. 💡 Main outcomes and implications: - ChatGPT, specifically GPT-4, can accurately and reliably answer heart failure-related questions. - ChatGPT may be a valuable resource for providing health-related information and education to patients with heart failure. - Further validation and testing are needed to ensure patient safety and equity across all demographics. 📚 Field significance: - Potential use of ChatGPT as a supplemental resource for patients with heart failure. - Importance of rigorous testing and validation for ChatGPT in healthcare settings. 🗄️: #datainsights #healthcare #heartfailure #ChatGPT

👉🏼 End-of-life Care Patient Information Leaflets-A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini 🤓 Prakash G Gondode 👇🏻 https://lnkd.in/evtrDVgG 🔍 Focus on data insights: - 📊 Google Gemini's patient information leaflets (PILs) demonstrated higher readability and actionability compared to those generated by ChatGPT. - 🤖 Both AI models produced content with positive sentiment, indicating a supportive tone in the information provided. - ✅ Accuracy and completeness were rated highly for both models, although Google Gemini had slightly lower accuracy scores. 💡 Main outcomes and implications: - 🌍 The study underscores the potential of AI in improving patient education, particularly in end-of-life care (EOLC), which is crucial for informed decision-making. - 🏥 Enhanced readability and actionability of AI-generated content can lead to better patient understanding and engagement in their care processes. - 🔄 Continuous improvement and innovation in AI-driven educational tools are essential to meet the diverse needs of patients across different cultural contexts. 📚 Field significance: - 💬 The findings contribute to the growing body of evidence supporting the integration of AI in healthcare communication, particularly in sensitive areas like EOLC. - 🧠 This research highlights the importance of culturally sensitive approaches in patient education, ensuring that information is accessible and relevant to diverse populations. - 📈 The study sets a precedent for future research on AI applications in healthcare, emphasizing the need for ongoing evaluation of AI-generated content quality. 🗄️: [#endoflifecare #patienteducation #artificialintelligence #readability #healthcarecommunication #culturalsensitivity #AIinHealthcare]

👉🏼 Comparison of Large Language Models in Diagnosis and Management of Challenging Clinical Cases 🤓 Sujeeth Krishna Shanmugam 👇🏻 https://lnkd.in/emSZFnpj 🔍 Focus on data insights: - 📊 The mean score of the LLMs was 19 ± 9 out of a maximum of 60, indicating variable performance among the models. - 🏆 Three LLMs

👉🏼 ChatGPT Responses to Frequently Asked Questions on Ménière's Disease: A Comparison to Clinical Practice Guideline Answers 🤓 Rebecca A Ho 👇🏻 https://lnkd.in/e7Nvpwzw 🔍 Focus on data insights: - ChatGPT-4.0 had significantly better resource reporting compared to ChatGPT-3.5. - Both models showed room for improvement in comprehensiveness, readability, and accuracy. 💡 Main outcomes and implications: - ChatGPT-4.0 demonstrated higher quality resources in responses. - Readability scores for both models fell below the recommended level. - Improvement needed in providing more comprehensive and less misleading information to patients. 📚 Field significance: - Evaluating AI models like ChatGPT against clinical guidelines can help identify areas for enhancement in patient education. - Enhancing AI models' readability and accuracy is crucial for effective patient communication in healthcare settings. 🗄️: [#Ménière'sDisease #AIModelEvaluation #PatientEducation #HealthcareCommunication]

👉🏼 The role of prompt engineering in enhancing ChatGPT's reliability for pediatric health information 🤓 Calissa J Leslie-Miller 👇🏻 https://lnkd.in/ey43etVT 🔍 Focus on data insights: - 📊 Prompt-engineered ChatGPT shows potential to influence parental decisions regarding medication, sleep, and diet. - 🤝 Participants rated the information produced by both ChatGPT and experts similarly in terms of various credibility measures. 💡 Main outcomes and implications: - 💬 Prompt-engineered outputs were perceived as more trustworthy and accurate than those from traditional experts when differences occurred. - 🧠 It is essential to integrate human expertise in the oversight of AI-generated healthcare information to maintain consumer trust. 📚 Field significance: - 🚨 The findings illustrate a crucial intersection between AI technology and healthcare communication, emphasizing the need for expert involvement in AI output. - 💡 This study may pave the way for future applications of AI in health information delivery while ensuring safety and reliability for consumers. 🗄️ [#pediatrics #healthinformation #AI #ChatGPT #promptengineering #trustworthiness #expertise #healthcaredecisions]

👉🏼 Comparing Provider and ChatGPT Responses to Breast Reconstruction Patient Questions in the Electronic Health Record 🤓 Daniel Soroudi 👇🏻 https://lnkd.in/eBGKedQw 🔍 Focus on data insights: - 📊 Combined provider responses had significantly better readability scores compared to all chatbot responses, indicating a potential gap in patient communication effectiveness when using AI. - 🤖 ChatGPT demonstrated higher empathy scores than human providers, suggesting AI can enhance emotional engagement with patients. - 🔍 There was no significant difference in accuracy between providers and chatbot responses, showcasing that AI can match professional accuracy under certain conditions. 💡 Main outcomes and implications: - ⚖️ The findings indicate that while ChatGPT is effective in empathy and accuracy, improvements are needed in readability for it to be a viable communication tool in clinical settings. - 🩺 This study suggests that integrating AI tools could alleviate some communication burdens on healthcare providers, potentially enhancing the overall patient experience. - 📈 The results advocate for further adjustments in AI tools to ensure they meet the necessary standards for patient education and support in medical decision-making. 📚 Field significance: - 🌐 The exploration of AI in patient communication opens avenues for improving healthcare delivery in diverse settings, particularly in specialty fields like plastic surgery. - 🧠 This research contributes to the ongoing discourse about the role of AI in healthcare, emphasizing both its capabilities and limitations. - 🛠️ The study serves as a foundation for future research on optimizing AI tools to better assist healthcare providers while maintaining high standards of patient interaction. 🗄️: [#patientcommunication #AIinhealthcare #breastreconstruction #ChatGPT #medicaleducation #empathy #readability #healthcareproviders #EHR]