Ankit Singh

👉🏼 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]

👉🏼 Fine-Tuned Bidirectional Encoder Representations From Transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study 🤓 Eunbeen Jo 👇🏻 https://lnkd.in/gx9UH89R 🔍 Focus on data insights: - 📊 ChatGPT demonstrated a 6.2% answer avoidance rate, indicating areas for improvement in responsiveness. - ⚖️ The fine-tuned KM-BERT model achieved an accuracy of 97.7%, highlighting its superior performance in medical specialty recommendations compared to ChatGPT's 93.9%. - 🚦 Precision and recall metrics indicate KM-BERT's effectiveness in correctly identifying relevant specialties, with precision at 57.0% and recall at 65.2%, whereas ChatGPT struggled with lower figures

👉🏼 Unveiling the e-Servicescape of ChatGPT: Exploring User Psychology and Engagement in AI-Powered Chatbot Experiences 🤓 Minseong Kim 👇🏻 https://lnkd.in/eZZeTE-Y 🔍 Focus on data insights: - 📊 The study utilized structural equation modeling to analyze user responses, revealing significant correlations between e-servicescape dimensions and emotional states. - 🧠 Negative emotions were notably influenced by factors such as security and visual appeal, while positive emotions were linked to customization and interactivity. - 📈 User satisfaction was found to be a critical mediator between emotional responses and the intention to engage with ChatGPT. 💡 Main outcomes and implications: - 🌐 The findings highlight the importance of e-servicescapes in shaping user experiences with AI technologies, suggesting that businesses should prioritize these elements to enhance user engagement. - 🔄 Understanding the emotional drivers can help marketers tailor their strategies to improve user satisfaction and retention in digital environments. - 💬 The research extends the concept of environmental psychology into the realm of AI, providing a framework for future studies on user interaction with digital services. 📚 Field significance: - 📖 This study contributes to the growing body of literature on digital environmental psychology, emphasizing the role of emotional states in technology adoption. - 🔍 It offers valuable insights for both scholars and practitioners in the fields of marketing and user experience design, particularly in the context of AI-driven services. - 🌟 The implications of this research can inform the development of more effective AI interfaces that cater to user emotional needs. 🗄️: [#eServicescape #UserExperience #EmotionalDesign #AI #Chatbots #DigitalPsychology #MarketingInsights #UserEngagement]

👉🏼 Scaffolding Learning: From Specific to Generic with Large Language Models 🤓 David S Yin 👇🏻 https://lnkd.in/eNKYVeZg 🔍 Focus on data insights: - 📊 Large language models

👉🏼 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]

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

👉🏼 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]

👉🏼 Pre-trained Language Models in Medicine: A Survey 🤓 Xudong Luo https://lnkd.in/eubyPuA5 🔍 Focus on data insights: - Pre-trained Language Models (PLMs) like BERT, BioBERT, and ChatGPT have shown potential in medical NLP tasks. - Survey covers achievements and methodologies of applying PLMs in medical NLP tasks. 💡 Main outcomes and implications: - Overview of basic concepts, methodologies, advantages, and steps of applying PLMs in medical NLP tasks. - Summary of recent research findings, strengths vs weaknesses, and potential limitations. 📚 Field significance: - Enhancing models' reliability, explainability, and fairness for promoting PLMs in clinical practice. 🗄️: #NaturalLanguageProcessing #PretrainedModels #MedicalNLP #DataInsights

👉🏼 Me-LLaMA: Foundation Large Language Models for Medical Applications 🤓 Qianqian Xie 👇🏻 https://lnkd.in/eQJREuAf 🔍 Focus on data insights: - Me-LLaMA models achieve better performance than existing open-source medical LLMs in zero-shot, few-shot, and supervised learning abilities. - Task-specific instruction tuning improves performance compared to other models. - Me-LLaMA models outperform ChatGPT and GPT-4 on various datasets. 💡 Main outcomes and implications: - Me-LLaMA is a significant advancement in medical language models, showcasing superior performance across general and medical tasks. - The study addresses limitations in applying LLMs to clinical settings due to a lack of specialized training on medical-specific data. - Me-LLaMA models mitigate the catastrophic forgetting problem better than other medical LLMs. 📚 Field significance: - Me-LLaMA introduces a novel approach to medical language models, leveraging large datasets and continual pre-training for improved performance. - The study highlights the potential of Me-LLaMA in revolutionizing medical applications and advancing AI in healthcare. 🗄️: [#medicalapplications #LLMs #datainsights #MeLLaMA #healthcareAI]