Alexandros Zenonos, Ph.D.

👉🏼 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

👉🏼 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

🔍 Exploring the Keys to LLM Success Large language models (LLMs) have shown impressive performance, but the factors behind these achievements are still evolving. Typically, enhancing reasoning in LLMs means increasing model size and context length, often leading to higher costs and delays. 📊 A recent study by Tenyx delves into the geometry of transformer layers in LLMs, identifying two crucial factors: 1️⃣ The density of token interactions within the multi-head attention module. 2️⃣ The relationship between model size, context length, and attention density for better reasoning. The research highlights a link between expressive power and reasoning abilities, suggesting that more complex inputs can boost LLM performance. Furthermore, higher intrinsic dimension in the final layers correlates with better reasoning skills, regardless of model size. 💡 This connection between approximation power and the number of regions influences LLMs' function approximation abilities, potentially enhancing their reasoning skills. Continued research could help smaller LLMs close the performance gap with their larger counterparts. Exciting times in the world of AI! 🚀 #AI #MachineLearning #LLM #Innovation #Research https://lnkd.in/eEyh5YHr

👉🏼 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]

👉🏼 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 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]

Excellent blog! Ways to fix exploding gradients problem in deep learning. Gradient Clipping is also an efficient method. https://lnkd.in/eXzkQp9v

👉🏼 LitAI: Enhancing Multimodal Literature Understanding and Mining with Generative AI 🤓 Gowtham Medisetti 👇🏻 https://lnkd.in/eKB__hFi 🔍 Focus on data insights: - 📊 The integration of optical character recognition

👉🏼 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

👉🏼 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]