Anand Kadumberi

👉🏼 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]

👉🏼 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

👉🏼 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]

👉🏼 Application of Deep Learning for Studying NMDA Receptors 🤓 Zhenfeng Deng 👇🏻 https://lnkd.in/e9tz7ACA 🔍 Focus on data insights: - Utilization of large pretrained models for studying NMDA receptors - Application of AI tools for drug development against CNS diseases - Prediction of blood-brain barrier permeability using AI 💡 Main outcomes and implications: - Enhanced understanding of NMDA receptor function through deep learning - Accelerated drug development process for CNS diseases - Improved prediction accuracy for BBB permeability 📚 Field significance: - Advancement in AI technology for pharmaceutical research - Potential for rapid and rational drug development in neuroscience 🗄️: [#ArtificialIntelligence #DeepLearning #NMDAReceptors #DrugDevelopment #CNSDiseases]

👉🏼 Correctable Landmark Discovery Via Large Models for Vision-Language Navigation 🤓 Bingqian Lin 👇🏻 https://lnkd.in/ezWpfYY6 🔍 Focus on data insights: - The CONSOLE framework leverages ChatGPT and CLIP models for open-world landmark discovery. - A learnable cooccurrence scoring module corrects the importance of each cooccurrence based on actual observations. - Observation enhancement strategy enhances action decision-making using corrected landmark features. 💡 Main outcomes and implications: - CONSOLE outperforms strong baselines on multiple VLN benchmarks, establishing new state-of-the-art results in unseen scenarios. - The proposed correctable landmark discovery scheme improves modality alignment in VLN tasks. - Integration of ChatGPT and CLIP models enhances landmark discovery based on commonsense priors. 📚 Field significance: - Advances in open-world sequential landmark discovery for Vision-Language Navigation. - Improved accuracy in aligning landmarks with visual observations in unexplored scenes. - Potential for enhancing navigation systems through data-driven insights. 🗄️: [#VisionLanguageNavigation #LandmarkDiscovery #DataInsights #MachineLearning]

👉🏼 Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges 🤓 Kai Kang 👇🏻 https://lnkd.in/et9B3HrG 🔍 Focus on data insights: - Large language models (LLMs) enhance educational outcomes by providing accessible code templates. - LLMs clarify the function of coding elements for medical students. - LLMs assist in error troubleshooting during bioinformatics education. 💡 Main outcomes and implications: - Practical applications of LLMs demonstrated through a case study on transcriptome sequencing data processing. - The reliability of LLM-generated content requires rigorous validation. - Integrating innovative LLMs with traditional methods prepares medical students for future bioinformatics challenges effectively. 📚 Field significance: - Transformative opportunities in bioinformatics education for medical students. - Enhancing educational outcomes through LLM integration. - Balancing accuracy and appropriateness of LLM-generated information for effective learning. 🗄️: #bioinformatics #medicalstudents #LLMs #education #datainsights

👉🏼 Benchmarking Large Language Models for Cervical Spondylosis 🤓 Boyan Zhang 👇🏻 https://lnkd.in/e5urWm3y 🔍 Focus on data insights: - 📊 The study analyzed frequently asked questions from patients, providing a comprehensive dataset for evaluating LLM responses. - 🧠 LLMs demonstrated varying levels of accuracy, with GPT-4 outperforming other models in terms of response quality. - 🔬 The evaluation was conducted by experienced spinal surgeons, ensuring a high standard for assessing the reliability of the information provided. 💡 Main outcomes and implications: - ✅ All evaluated LLMs were able to provide satisfactory answers, indicating their potential utility in patient education and support. - ⚖️ The findings suggest that while LLMs can assist in medical inquiries, there are still limitations that need to be addressed for clinical use. - 🚀 The study highlights the importance of continuous improvement in AI models to enhance their accuracy and applicability in healthcare settings. 📚 Field significance: - 🌐 This research underscores the growing role of artificial intelligence in medicine, particularly in enhancing patient access to information. - 🔍 It opens avenues for further exploration into the integration of LLMs in clinical practice, potentially transforming patient-provider interactions. - 📈 The results contribute to the ongoing discourse on the ethical and practical implications of using AI in healthcare. 🗄️: [#CervicalSpondylosis #LargeLanguageModels #ArtificialIntelligence #PatientEducation #MedicalAdvice #HealthcareInnovation #AIinMedicine]

👉🏼 ChatGPT-4 Performs Clinical Information Retrieval Tasks Utilizing Consistently More Trustworthy Resources Than Does Google Search for Queries Concerning the Latarjet Procedure 🤓 Jacob F Oeding 👇🏻 https://lnkd.in/eXi69Fdk 🔍 Focus on data insights: - ChatGPT-4 had a mean accuracy of 2.9±0.9 for numeric-based answers, outperforming Google's 2.5±1.4 (p=0.65). - ChatGPT-4 sourced information solely from academic resources, significantly different from Google's use of non-academic sources. - 40% of FAQs were identical between ChatGPT-4 and Google, showcasing comparable general information retrieval capabilities. 💡 Main outcomes and implications: - ChatGPT-4 provided accurate and reliable information on the Latarjet procedure, relying on academic sources exclusively. - Google Search Engine often utilized individual surgeon and medical practice websites, potentially impacting information reliability. - Despite differences in sourcing, both platforms offered clinically relevant and accurate information to users. 📚 Field significance: - Information retrieval tools like ChatGPT-4 can enhance patient education and understanding of medical procedures. - Emphasizes the importance of utilizing trustworthy academic sources for accurate medical information dissemination. 🗄️: [#clinicalinformatics #informationretrieval #medicalresearch]

👉🏼 An Autonomous AI Agent for Streamlined Multi-Omic Bioinformatics Analysis 🤓 Juexiao Zhou 👇🏻 https://lnkd.in/ehb4r6iB 🔍 Focus on data insights: - 📊 AutoBA utilizes large language models

👉🏼 Evaluating the strengths and weaknesses of large language models in answering neurophysiology questions 🤓 Hassan Shojaee-Mend 👇🏻 https://lnkd.in/eZEbA_zF 🔍 Focus on data insights: - Large language models (LLMs) showcase remarkable natural language processing capabilities. - Evaluation of LLMs' proficiency in neurophysiology is crucial for research, education, and clinical applications. - Assessment of LLMs in answering neurophysiology questions in English and Persian (Farsi) reveals insights into their utility. 💡 Main outcomes and implications: - LLMs demonstrate good performance in answering neurophysiology questions. - No significant difference observed between English and Persian languages or cognitive levels. - Models excel in motor system topics but show weaknesses in integrative topics. 📚 Field significance: - Understanding LLMs' capabilities and limitations in neurophysiology research. - Highlighting the need for targeted training to address knowledge gaps and enhance reasoning skills. - Importance of domain-specific assessments for evaluating advancements in LLMs' performance. 🗄️: [#naturallanguageprocessing #neurophysiology #LLMs #research #datainsights]

👉🏼 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]