Stewart Whiting

👉🏼 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]

👉🏼 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

👉🏼 Performance of Novel GPT-4 in Otolaryngology Knowledge Assessment 🤓 Lucy Revercomb 👇🏻 [Link to Article](https://lnkd.in/eKD5MTse) 🔍 Focus on data insights: - 📊 GPT-4 achieved a 72.0% accuracy rate, significantly outperforming GPT-3.5, which scored only 51.3%. - 🔬 A specialized custom GPT-4 model had an improved accuracy of 81.3%, highlighting the effectiveness of tailoring AI for specific fields. - ⚖️ Statistical analysis revealed that the customized model had 2.19 times higher odds of answering questions correctly compared to the standard GPT-4. 💡 Main outcomes and implications: - ✅ The study illustrates that enhanced AI models like GPT-4 can substantially impact medical education by providing more accurate assessments of knowledge. - 📚 Customization is key; AI models designed with specific educational goals in mind can yield better outcomes in student learning and assessment. - 🧑‍⚕️ This advancement suggests a shift toward integrating AI technology in professional training programs, potentially revolutionizing how medical knowledge is taught and evaluated. 📚 Field significance: - 🏥 The findings support the integration of advanced AI in clinical education, improving readiness for board examinations in specialties like otolaryngology. - 🌐 Results may encourage further research into specialized AI applications across various medical fields, leading to broader educational improvements. - 🚀 As AI continues to evolve, its role in enhancing medical competencies and education will likely become increasingly critical. 🗄️: [#GPT4] [#Otolaryngology] [#MedicalEducation] [#AIinHealthcare] [#KnowledgeAssessment]

👉🏼 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]

👉🏼 Understanding the Impact of Large Language Models in Healthcare Communication 🤓 Zefeng Yang 👇🏻 https://lnkd.in/eAtYVexV 🔍 Focus on data insights: - 📊 Large language models (LLMs) demonstrate advanced capabilities in natural language comprehension and generation, enhancing communication in healthcare settings. - 🧠 The evolution of LLMs parallels significant advancements in deep learning, particularly through transformer architecture, which improves their functionality. - 🔄 With extensive training data, LLMs can retain memory and comprehend complex medical information, facilitating better interactions between clinicians and patients. 💡 Main outcomes and implications: - 📝 LLMs can automate medical documentation, potentially reducing administrative burdens on healthcare providers. - 🤖 Future applications may include autonomous diagnosis and treatment suggestions, improving efficiency in patient care. - 📚 LLMs can assist in triage, summarizing medical documents, and personalizing patient education materials, thereby enhancing patient understanding and engagement. 📚 Field significance: - 🌐 The integration of LLMs in healthcare could revolutionize communication, leading to improved patient outcomes and satisfaction. - 🔍 Ongoing research is necessary to address limitations and ensure the safe and effective use of LLMs in real-world clinical settings. - 🚀 As technology advances, LLMs are poised to play a critical role in the future of healthcare delivery, particularly in specialized fields like ophthalmology. 🗄️: [#LargeLanguageModels #HealthcareCommunication #NaturalLanguageProcessing #DeepLearning #PatientCare #MedicalDocumentation #Ophthalmology #AIinHealthcare]

👉🏼 Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data 🤓 Benjamin G Cohen 👇🏻 https://lnkd.in/erTv89Nx 🔍 Focus on data insights: - 📈 The proposed framework identifies free-form partial differential equations

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

👉🏼 ChatGPT-based Biological and Psychological Data Imputation 🤓 Anam Nazir https://lnkd.in/ezAXTbzp 🔍 Focus on data insights: - ChatGPT utilized for biological and psychological data imputation - Evaluation metrics include Pearson correlation coefficient, relative accuracy, and mean absolute error 💡 Main outcomes and implications: - ChatGPT shows superior efficacy compared to traditional imputation methods - Customized data-to-text prompting enhances accuracy in imputations 📚 Field significance: - Improved accuracy in imputing biological and psychological data - Potential for enhancing research outcomes in large cohort studies 🗄️: #datainsights #imputation #biologicaldata #psychologicaldata

👉🏼 How deep learning is transforming protein structural biology through advanced neural networks 🤓 Matthias Bochtler 👇🏻 https://lnkd.in/g9HaXxTN 🔍 Focus on data insights: - 📈 Deep neural networks like CNNs, LLMs, and GNNs are significantly enhancing protein structure prediction. - 🧬 Applications of AI are expanding in areas such as inverse folding and protein design, leading to more effective and efficient workflows. - 🤖 Machine learning models are being leveraged to translate complex biological data into understandable and actionable insights. 💡 Main outcomes and implications: - 🔄 The integration of advanced deep learning techniques is revolutionizing the methodology used in structural biology. - 🌐 This transformation highlights the necessity for structural biologists to adapt to new technologies for improved research outcomes. - 📊 Insights derived from AI applications are likely to accelerate discoveries in protein-related biological processes and therapeutics. 📚 Field significance: - 🔍 The convergence of artificial intelligence and structural biology is setting a new paradigm in the field, facilitating unprecedented levels of discovery. - 💡 This review serves as a comprehensive guide, not only for structural biologists but also for those with backgrounds in machine learning, physics, or statistics. - 📖 As new methodologies emerge, continuous education and adaptation will be essential for scientists to remain at the forefront of research. 🗄️: [#deepLearning] [#neuralNetworks] [#structuralBiology] [#proteinDesign] [#machineLearning] [#AI] [#CNN] [#LLM] [#GNN]

👉🏼 GENEVIC: GENetic data exploration and visualization via intelligent interactive console 🤓 Anindita Nath 👇🏻 https://lnkd.in/ejQG-BX5 🔍 Focus on data insights: - 📊 GENEVIC automates the analysis of genetic data, significantly reducing the time researchers spend on data interpretation. - 🔍 It integrates multiple data sources, allowing for comprehensive searches across PubMed, Google Scholar, and arXiv, enhancing literature review efficiency. - 🧬 The tool prioritizes genetic variants linked to complex diseases, aiding in targeted research and potential therapeutic developments. 💡 Main outcomes and implications: - 🚀 By streamlining the process of genetic data exploration, GENEVIC empowers researchers to make informed decisions quickly, potentially accelerating biomedical discoveries. - 🛠️ The user-friendly interface democratizes access to advanced genetic analysis tools, making them available to researchers without specialized training. - 🔒 With HIPAA-compliant infrastructure, GENEVIC ensures that sensitive genetic data is handled securely, fostering trust in its use within the biomedical community. 📚 Field significance: - 🌐 GENEVIC represents a significant advancement in bioinformatics, bridging the gap between data generation and actionable biomedical insights. - 🧩 The integration of generative AI into genetic research tools marks a transformative shift in how researchers interact with complex datasets. - 📈 As a prototype, GENEVIC sets a precedent for future innovations in genetic data analysis, paving the way for more sophisticated tools in the field. 🗄️: [#bioinformatics #geneticdata #AI #biomedicalresearch #dataanalysis #geneticvariants #healthcare #machinelearning]

Investigating generative AI models and detection techniques: impacts of tokenization and dataset size on identification of AI-generated text - Generative AI models like ChatGPT, Gemini, and Claude are increasingly utilized in K-12 education, providing valuable support across various subjects. - Current detection methods, such as GPT-Zero, face challenges in identifying modified AI-generated texts, particularly for English as a Second Language learners. - The study explores the relationship between dataset size and model performance, emphasizing the need for effective data collection in future research. DOI: https://lnkd.in/g9vx7zd3 DOI: pmc:PMC11611853