Tamer Chowdhury

👉🏼 Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation 🤓 Jimyung Park 👇🏻 https://lnkd.in/eEvH3Xhi 🔍 Focus on data insights: - GPT-4 achieved an F1-score of 0.891 in concept extraction. - 29 errors were detected in SQL generation, with logic errors being the most common. - Reasoning evaluations showed high coherence but relatively lower readability. 💡 Main outcomes and implications: - GPT-4 significantly improves the accuracy of extracting clinical trial eligibility criteria concepts in C2Q 3.0. - Continued research is warranted to ensure the reliability of large language models. 📚 Field significance: - Automation of patient identification in clinical research. - Advancements in leveraging large language models for query generation. 🗄️: #clinicalresearch #GPT-4 #datainsights #automation

👉🏼 Unlocking the potential of advanced large language models in medication review and reconciliation: A proof-of-concept investigation 🤓 Kannan Sridharan 👇🏻 https://lnkd.in/ecpgck5v 🔍 Focus on data insights: - 📊 The study evaluated four large language models

👉🏼 Are large language models a useful resource to address common patient concerns on hallux valgus? A readability analysis 🤓 William J Hlavinka 👇🏻 https://lnkd.in/eycmZEyq 🔍 Focus on data insights: - 📊 ChatGPT-3.5 and 4.0 provided longer responses compared to Google, with an average of 315 ± 39 and 294 ± 39 words, respectively. - 📈 Significant differences in readability were observed, with both versions of ChatGPT exceeding the seventh to eighth-grade reading level. - 📝 The Flesch-Kincaid Reading Ease scores indicated that ChatGPT's responses were less accessible than those from Google. 💡 Main outcomes and implications: - 🔍 The study highlights the potential of AI models like ChatGPT to provide detailed information, but raises concerns about readability for general audiences. - ⚖️ Healthcare providers may need to consider the complexity of AI-generated content when using it as a resource for patient education. - 🗣️ There is a need for further research to optimize AI responses for better comprehension among patients. 📚 Field significance: - 🌐 This research contributes to the ongoing discussion about the role of AI in healthcare communication and patient education. - 🧠 It underscores the importance of balancing detail and accessibility in medical information dissemination. - 📚 The findings can inform future developments in AI tools aimed at improving patient understanding of medical procedures. 🗄️: [#AI #Healthcare #PatientEducation #Readability #ChatGPT #BunionSurgery #MedicalCommunication]

👉🏼 A comparison of drug information question responses by a drug information center and by ChatGPT 🤓 Samantha Triplett 👇🏻 https://lnkd.in/ezjYATsW 🔍 Focus on data insights: - 📊 ChatGPT demonstrated an overall accuracy rate of 50% in responding to drug information inquiries. - 📝 Clarity and readability of ChatGPT responses were rated higher than those from the drug information center

👉🏼 Vaccination hesitancy: agreement between WHO and ChatGPT-4.0 or Gemini Advanced 🤓 Matteo Fiore 👇🏻 https://lnkd.in/etYWncWA 🔍 Focus on data insights: - 📊 Both ChatGPT-4.0 and Google Gemini Advanced provided 94.7% appropriate responses to vaccine-related questions compared to WHO guidelines. - 🧠 Responses were coherent and aligned with current health recommendations, showcasing AI's potential as a reliable information source. - 🌐 The chatbots encouraged users to consult credible sources, demonstrating a responsible approach to disseminating health information. 💡 Main outcomes and implications: - 💬 The high accuracy rate implies that AI chatbots can effectively support primary prevention strategies in public health. - 📈 Improved health literacy and medication adherence may result from utilizing these tools for addressing vaccine hesitancy. - 🔍 Continuous monitoring of chatbot performance is essential to maintain their reliability and accuracy over time. 📚 Field significance: - 🌟 The findings suggest a shift in communication methods within healthcare, integrating advanced technology to enhance public engagement. - 🏥 This advancement could bridge gaps in health education, particularly in addressing misconceptions about vaccines. - 💡 AI integration in health communication could pave the way for more innovative solutions to combat misinformation. 🗄️: [#Vaccination #AI #Chatbots #PublicHealth #WHO #HealthLiteracy #VaccineHesitancy #DataInsights]

Thoughts on this? >> New Geekbench AI benchmark can test the performance of CPUs, GPUs, and NPUs >> Comment below! >>> lqventures.com #digitalhealth #socialmedia #digitalmarketing #industry40 #mhealth #healthtech #AI #IoT

👉🏼 Developing large language models to detect adverse drug events in posts on X 🤓 Yu Deng 👇🏻 https://lnkd.in/eXURsJZs 🔍 Focus on data insights: - 📊 The study demonstrates the effectiveness of large language models

👉🏼 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]

👉🏼 Enhancing Diabetes Management for Older Patients: The Potential Role of ChatGPT 🤓 Ken Tanaka 👇🏻 https://lnkd.in/ef-cpgSY 🔍 Focus on data insights: - AI technology enhances diabetes education for older individuals - Importance of accuracy in AI integration into healthcare 💡 Main outcomes and implications: - Improved diabetes management for older patients - Need for establishing a framework for AI in healthcare 📚 Field significance: - Direct communication between patients and healthcare providers remains crucial - Technological advancements in diabetes management 🗄️: [#diabetesmanagement #AIintegration #healthcareframework]

👉🏼 Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data 🤓 Ramya Padmavathy Radha Krishnan 👇🏻 https://lnkd.in/e4-kfJtf 🔍 Focus on data insights: - 📊 ChatGPT's predictive ability for drug-drug interactions

👉🏼 Drug repurposing in status epilepticus 🤓 Matthew C Walker 👇🏻 https://lnkd.in/epHeWdNP 🔍 Focus on data insights: - 📊 Integration of biological knowledge and computational methods enhances drug repurposing efficacy. - 🧠 Artificial intelligence, including platforms like ChatGPT, streamlines the identification of potential drug candidates for SE. - 🔬 In silico analysis of gene expression changes aids in predicting molecular targets for treatment. 💡 Main outcomes and implications: - ✅ Significant advantages of drug repurposing include reduced development time, lower costs, and higher success rates compared to novel drug development. - 🏷️ Identified candidate drugs for SE treatment include metformin, sirolimus, and riluzole, which can lead to new therapeutic options. - ⚖️ Ongoing challenges such as intellectual property issues and regulatory barriers must be addressed to fully utilize drug repurposing strategies. 📚 Field significance: - 🌍 Drug repurposing represents a viable solution to the current stagnation in new drug development for status epilepticus, potentially improving patient outcomes. - 🛠️ Emphasizes the need for innovative approaches in pharmacology to overcome logistical hurdles in drug development for complex conditions. - 📈 Sets a precedent for future research into drug repurposing as an efficient pathway to swiftly address unmet medical needs in various disease areas. 🗄️: [#drugrepurposing #statusepilepticus #AI #computationalmethods #pharmacology #clinicalresearch #patientoutcomes #therapeutics] <div><p style="color: #4aa564;">Epilepsy Behav. 2024 Oct 27;161:110109. doi: 10.1016/j.yebeh.2024.110109. Online ahead of print.</p><p><b>ABSTRACT</b></p><p>The treatment of status epilepticus (SE) has changed little in the last 20 years, largely because of the high risks and costs of new drug development for SE. Moreover, SE poses specific challenges to drug development, such as patient diversity, logistical hurdles, and the need for acute treatment strategies that differ from chronic seizure prevention. This has reduced the appetite of industry to develop new drugs in this area. Drug repurposing is an attractive approach to address this unmet need...</p></div>

👉🏼 A comparison of drug information question responses by a drug information center and by ChatGPT 🤓 Samantha Triplett 👇🏻 https://lnkd.in/gz2QtiuH 🔍 Focus on data insights: - 📊 ChatGPT achieved an overall accuracy rate of 50% in responding to drug information questions compared to the Drug Information Center

👉🏼 Developing large language models to detect adverse drug events in posts on X 🤓 Yu Deng 👇🏻 https://lnkd.in/eWitZYX5 🔍 Focus on data insights: - 📊 Large Language Models

👉🏼 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]

👉🏼 Text summarization with ChatGPT for drug labeling documents 🤓 Lan Ying 👇🏻 https://lnkd.in/e-tvY6Pc 🔍 Focus on data insights: - ChatGPT-generated summaries closely resembled those produced by human experts - Greater similarity observed in summarizing drug safety information 💡 Main outcomes and implications: - ChatGPT's potential to accelerate work in critical areas, including drug safety 📚 Field significance: - Automation of text summarization using large language models like ChatGPT - Improved efficiency and accuracy in summarizing drug labeling documents 🗄️: [#drugdiscovery #textsummarization #ChatGPT #datainsights]