Miguel Kirsch

👉🏼 Evaluating accuracy and reproducibility of ChatGPT responses to patient-based questions in Ophthalmology: An observational study 🤓 Asem A Alqudah 👇🏻 https://lnkd.in/e5tnmAmw 🔍 Focus on data insights: - 📊 ChatGPT provided comprehensive responses to 59.8% of the ophthalmology questions evaluated. - 🔄 The model demonstrated a high reproducibility rate of 91.5% across multiple responses for the same questions. - 🧠 The grading scale used allowed for nuanced evaluation of response quality, highlighting areas for improvement. 💡 Main outcomes and implications: - ⚖️ The study indicates that while ChatGPT can serve as a supplementary source of health information, it should not replace professional medical advice. - 🔍 The moderate accuracy suggests a need for further refinement and validation of AI-generated health information. - 🛠️ Future modifications to the model could enhance its reliability and applicability in clinical settings. 📚 Field significance: - 🌐 The findings contribute to the ongoing discourse on the role of AI in healthcare, particularly in patient education. - 🏥 Understanding the limitations of AI tools like ChatGPT is crucial for integrating them into clinical practice safely. - 📈 This research underscores the importance of continuous evaluation of AI systems to ensure they meet the needs of patients and healthcare providers. 🗄️: [#ChatGPT #Ophthalmology #AIinHealthcare #PatientEducation #HealthInformation #Accuracy #Reproducibility #MedicalAdvice]

👉🏼 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]

👉🏼 Assessing the quality and readability of online patient information: ENT UK patient information e-leaflets vs responses by a Generative Artificial Intelligence 🤓 Eamon Shamil 👇🏻 https://lnkd.in/e8zxBEEd 🔍 Focus on data insights: - 📊 The median EQIP score for ENT UK leaflets was 23, indicating moderate quality. - 📉 Non-specialist doctors rated the leaflets higher than medical students, highlighting variability in assessment based on expertise. - ✍️ ChatGPT responses demonstrated uniform quality across different topics, yet were less readable compared to professionally-produced leaflets. 💡 Main outcomes and implications: - ⚖️ Both ChatGPT-generated content and professional leaflets provided comparable information quality, but varied significantly in readability levels. - 📖 The findings stress the importance of readability in patient education materials, particularly as more individuals seek online health information. - 🌐 With the rise of AI in healthcare, there is a critical need for developing guidelines that ensure both informative and accessible patient resources. 📚 Field significance: - 🔍 This study serves as a foundational analysis of how AI can be integrated into patient education, emphasizing the continued need for human oversight. - 📈 It opens pathways for future research into improving the clarity and effectiveness of AI-generated health information. - 📘 The implications extend to policy-making, advocating for improved standards in digital health communication to better serve patients. 🗄️: [#AI #healthcare #patienteducation #readability #dataanalysis #ENT #ChatGPT #informationquality]

👉🏼 Readability and Information Quality in Cancer Information From a Free vs Paid Chatbot 🤓 David Musheyev 👇🏻 https://lnkd.in/eggxUgeE 🔍 Focus on data insights: - 📊 The study evaluated 100 chatbot responses, revealing that nonprompted free chatbot responses had lower readability compared to paywalled ones. - 🔍 Prompting the free chatbot significantly improved its readability scores, indicating that user input can enhance information clarity. - 📈 Both free and paywalled chatbots showed increased readability when prompted, suggesting that prompting is a valuable tool for improving communication. 💡 Main outcomes and implications: - 🏷️ Paying for chatbot services resulted in easier-to-read responses, which may influence consumer choices regarding health information sources. - 💬 Educating users on effective prompting techniques could democratize access to high-quality health information, making it more accessible to a broader audience. - ⚖️ The lack of significant differences in overall information quality between the two models suggests that cost does not necessarily equate to better information. 📚 Field significance: - 🌐 This research highlights the importance of readability in health communication, particularly in the context of emerging technologies like chatbots. - 🧠 Understanding how to effectively use chatbots can empower users to obtain clearer and more useful health information. - 🔗 The findings underscore the need for ongoing research into the usability and effectiveness of AI-driven health information tools. 🗄️: [#readability #healthinformation #chatbots #AI #consumerhealth #publichealth #informationquality #digitalhealth]

👉🏼 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

👉🏼 Readability analysis of ChatGPT's responses on lung cancer 🤓 Adem Gencer 👇🏻 https://lnkd.in/eqA97iKB 🔍 Focus on data insights: - 📊 The study evaluated the readability of ChatGPT-generated responses using various readability formulas, revealing a moderate level of complexity. - 📝 A total of 80 questions related to lung cancer were analyzed, highlighting the need for accessible medical information. - 🔍 The mean scores across different readability scales indicated that the responses were often difficult to read, suggesting a gap in communication effectiveness. 💡 Main outcomes and implications: - 📈 The findings suggest that while AI tools like ChatGPT can provide valuable information, their outputs may not be suitable for all audiences, particularly those with lower educational backgrounds. - ⚖️ There is an implication for developers to enhance AI models to generate content that is more accessible and understandable for diverse populations. - 🧠 The study underscores the importance of tailoring medical information to meet the needs of patients and healthcare professionals alike. 📚 Field significance: - 🌐 This research contributes to the ongoing discourse on the role of AI in healthcare communication, emphasizing the necessity for clarity in patient education. - 💬 It highlights the potential of AI tools to bridge information gaps but also points out the challenges in ensuring that such tools are user-friendly. - 🔗 The study serves as a foundation for future research aimed at improving the readability and accessibility of AI-generated medical content. 🗄️: [#lungcancer #AI #ChatGPT #readability #healthcarecommunication #patienteducation #medicalinformation #accessibility]

Improving Prediction of Complications Post-Proton Therapy in Lung Cancer Using Large Language Models and Meta-Analysis This study enhances the efficiency of predicting complications in lung cancer patients receiving proton therapy by utilizing large language models (LLMs) and meta-analytical techniques for literature quality assessment. - Overall effect size for model predictions: 0.78 - High heterogeneity observed: I² = 72.88%, P < 0.001 - Predictive effect sizes for radiation-induced esophagitis: 0.79; pneumonitis: 0.77 - ChatGPT-4 accuracy: 90% (highest among evaluated models) - Llama3 and Llama2 accuracies: 44% to 62% - LLM evaluations conducted 3229 times faster than manual assessments - Risk assessment identified: 9 studies high risk, 3 low risk, 1 unknown Conclusion: The integration of large language models with meta-analysis techniques significantly Link: https://lnkd.in/eKAXMMXb

Improving Prediction of Complications Post-Proton Therapy in Lung Cancer Using Large Language Models and Meta-Analysis This study enhances the efficiency of predicting complications in lung cancer patients receiving proton therapy by utilizing large language models (LLMs) and meta-analytical techniques for literature quality assessment. - Overall effect size for model predictions: 0.78 - High heterogeneity observed: I² = 72.88%, P < 0.001 - Predictive effect sizes for radiation-induced esophagitis: 0.79; pneumonitis: 0.77 - ChatGPT-4 accuracy: 90% (highest among evaluated models) - Llama2 and Llama3 accuracies: 44% to 62% - LLM evaluations conducted 3229 times faster than manual assessments - Risk assessment: 9 studies high risk, 3 low risk, 1 unknown Conclusion: The integration of LLMs with meta-analysis techniques significantly increases Link: https://lnkd.in/exMpyx7j

👉🏼 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]