Kevin Chih-Yao Huang

👉🏼 An Autonomous AI Agent for Streamlined Multi-Omic Bioinformatics 🤓 Juexiao Zhou 👇🏻 https://lnkd.in/egsngA94 🔍 Focus on data insights: - 📊 AutoBA leverages large language models

👉🏼 Prompt engineering on leveraging large language models in generating response to InBasket messages 🤓 Sherry Yan 👇🏻 https://lnkd.in/eZpReUeH 🔍 Focus on data insights: - 📈 The study demonstrated a significant increase in physician acceptance of LLM-generated draft responses, rising from 62% to 84% after three iterations of prompt engineering. - 🗣️ Patients reported a notable improvement in the tone (78%) and overall quality (80%) of messages generated by the optimized prompts compared to the original ones. - 🤖 A majority of patients (76%) could not distinguish between human and LLM-generated responses, indicating high-quality output from the model. 💡 Main outcomes and implications: - 🔄 Iterative prompt engineering proved effective in enhancing the quality of responses to Patient Medical Advice Requests (PMARs), suggesting a viable method for improving clinician-patient communication. - 🧠 Clinicians expressed that the use of LLMs could significantly reduce cognitive load when managing InBasket messages, potentially leading to improved efficiency in clinical settings. - 💬 The findings highlight the potential for LLMs to bridge gaps in patient care by providing timely and relevant information, thus improving patient satisfaction. 📚 Field significance: - 🌐 This research underscores the transformative role of AI and LLMs in healthcare, particularly in managing communication and administrative tasks. - 🏥 The study contributes to the growing body of evidence supporting the integration of AI tools in clinical workflows, paving the way for future innovations in patient care. - 📊 It emphasizes the importance of user feedback in refining AI applications, ensuring that they meet the needs of both clinicians and patients effectively. 🗄️: [#AI #LargeLanguageModels #Healthcare #PatientCare #PromptEngineering #ClinicalCommunication #CognitiveLoad #PatientSatisfaction #InBasketMessages]

👉🏼 Accuracy, readability, and understandability of large language models for prostate cancer information to the public 🤓 Jacob S Hershenhouse 👇🏻 https://lnkd.in/ek8wbVfg 🔍 Focus on data insights: - GPT-generated output was deemed correct by 71.7% to 94.3% of raters across 9 scenarios. - GPT-generated simplified layperson summaries were rated as accurate in 8 of 9 scenarios. - Mean readability of layperson summaries was significantly higher than original GPT outputs. 💡 Main outcomes and implications: - GPT shows promise for correct patient education for prostate cancer-related content. - Enhancing accuracy, completeness, clarity, and readability of GPT responses can improve its utility in medical chatbots. 📚 Field significance: - Improving the quality of information provided by large language models can enhance patient education and decision-making in healthcare. - Optimizing language model outputs for patient consumption can bridge the gap between complex medical information and public understanding. 🗄️: [#prostatecancer #GPT #patienteducation #healthcare #medicalchatbots]

👉🏼 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]

👉🏼 Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties 🤓 Felix Busch 👇🏻 https://lnkd.in/g6Gwkcf8 🔍 Focus on data insights: - GPT-4V demonstrated superior performance compared to GPT-4 in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection. - The integration of text and image analysis provided enhanced insights into radiology subspecialties. - Data-driven approaches utilizing GPT-4V showcased promising results for advanced radiological applications. 💡 Main outcomes and implications: - Improved accuracy and efficiency in radiological image analysis. - Enhanced diagnostic capabilities and decision-making support for radiologists. - Potential for increased automation and standardization in radiological practices. 📚 Field significance: - Advancements in artificial intelligence and natural language processing technologies are revolutionizing radiological applications. - Integration of text and image analysis tools can significantly impact the efficiency and accuracy of radiological diagnoses. - Data-driven insights from GPT-4V have the potential to reshape the future of radiology practice. 🗄️: #radiology #AI #textanalysis #imageanalysis #GPT-4V

👉🏼 Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems 🤓 Syed Ali Haider 👇🏻 https://lnkd.in/eb3hb8se 🔍 Focus on data insights: - 📊 Gemini achieved 98% accuracy in breast classification, outperforming ChatGPT-4's 71%. - 🧠 Both models excelled in specific classifications, indicating strengths in certain areas of medical diagnostics. - 🔬 The study utilized fifty clinical scenarios to rigorously assess LLM performance across five established classification systems. 💡 Main outcomes and implications: - 🚀 The superior performance of Gemini suggests its potential as a valuable tool for plastic surgeons in diagnostic processes. - 🏥 Enhanced accuracy in classification can lead to better-informed treatment decisions, ultimately improving patient outcomes. - 🔄 Integration of LLMs into clinical practice may revolutionize diagnostic support in the field of plastic surgery. 📚 Field significance: - 🌟 This research highlights the growing role of artificial intelligence in medicine, particularly in enhancing diagnostic accuracy. - 🔗 The findings could pave the way for further studies on LLM applications in various medical specialties. - 📈 As LLM technology evolves, its integration into healthcare practices may significantly impact patient care and surgical outcomes. 🗄️: [#diagnostics #artificialintelligence #machinelearning #plasticurgery #breastclassification #medicalresearch #patientoutcomes #healthcareinnovation]

👉🏼 Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model 🤓 Daisuke Ono 👇🏻 https://lnkd.in/e2RK6-xr 🔍 Focus on data insights: - GPT-4Vision accurately recognized staining techniques and tissue origin but struggled with specific lesion identification. - The interpretation of images was notably influenced by the provided textual context, sometimes leading to diagnostic inaccuracies. - Few-shot learning significantly improved GPT-4Vision's diagnostic capabilities, enhancing accuracy from 40% in zero-shot learning to 90% with 20-shot learning. 💡 Main outcomes and implications: - GPT-4Vision faces challenges in independently interpreting histopathological images. - Few-shot learning is a promising approach to enhance GPT-4Vision's performance, especially in neuropathology where acquiring extensive labeled datasets is challenging. 📚 Field significance: - Artificial intelligence advancements like GPT-4Vision have expanded the potential for medical image interpretation. - Comparative analysis with traditional convolutional neural network models provides insights into improving diagnostic accuracy in neurodegenerative disease histopathology. 🗄️: #neurodegenerative #histopathology #GPT-4Vision #fewshotlearning