Mo Hassanpour

👉🏼 Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges 🤓 Kai Kang 👇🏻 https://lnkd.in/gEEkehBw 🔍 Focus on data insights: - Large language models (LLMs) enhance educational outcomes by providing accessible code templates. - LLMs clarify the function of coding elements in bioinformatics education. - LLMs assist in error troubleshooting during data analysis processes. 💡 Main outcomes and implications: - The integration of LLMs in bioinformatics education improves student learning experiences. - LLMs can streamline data processing tasks in medical research. - Rigorous validation is essential to ensure the accuracy and reliability of LLM-generated information. 📚 Field significance: - Integrating innovative LLMs with traditional educational methods prepares medical students effectively for future challenges in bioinformatics. 🗄️: [#bioinformatics #medicalstudents #LLMs #datainsights]

👉🏼 Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study 🤓 Daun Shin 👇🏻 https://lnkd.in/eMKKGjb5 🔍 Focus on data insights: - 📊 The study leveraged user-generated diary text data, demonstrating the utility of emotional expression in detecting clinical depression. - 🤖 Utilization of advanced Large Language Models

👉🏼 Benchmarking State-of-the-Art Large Language Models for Migraine Patient Education: A Comparison of Performances on the Responses to Common Queries 🤓 Linger Li 👇🏻 https://lnkd.in/eEhvYdia 🔍 Focus on data insights: - Evaluation of responses from five leading LLMs on 30 commonly asked migraine-related queries. - Varied levels of accuracy among LLMs, with ChatGPT-4.0 providing the highest appropriate responses at 96.7%. - Google Bard had a 'poor' rating proportion of 6.7%, while other LLMs ranged from 3.3% to 6.7%. 💡 Main outcomes and implications: - Potential of LLMs to accurately address common migraine-related queries. - Advancement in AI-assisted education for individuals with migraine. - Insights for a holistic approach to migraine management. 📚 Field significance: - Enhanced education for individuals with migraine. - Utilization of LLMs in healthcare for improved patient education. - Implications for AI applications in managing chronic disorders. 🗄️: #migraine #LLMs #AI-assisted education #healthcare #patient management

👉🏼 Fine-tuning large language models for chemical text mining 🤓 Wei Zhang 👇🏻 https://lnkd.in/eMqfNb2d 🔍 Focus on data insights: - Fine-tuned large language models (LLMs) demonstrated impressive performance in various chemical text mining tasks. - Reduced the need for repetitive and extensive prompt engineering experiments. - Achieved high accuracy levels with minimal annotated data. 💡 Main outcomes and implications: - Fine-tuned ChatGPT models excelled in all tasks, outperforming other LLMs. - Versatile, robust, and low-code capability of fine-tuned LLMs can revolutionize chemical knowledge extraction. - Potential for automated data acquisition in the field of chemical text mining. 📚 Field significance: - Advancements in utilizing fine-tuned LLMs for complex chemical text mining tasks. - Potential to enhance efficiency and accuracy in extracting chemical knowledge from diverse texts. 🗄️: [#chemicaltextmining, #finetuning, #LLMs, #datainsights]

👉🏼 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

Great insight on FineWeb

👉🏼 Utilizing ChatGPT as a scientific reasoning engine to differentiate conflicting evidence and summarize challenges in controversial clinical questions 🤓 Shiyao Xie 👇🏻 https://lnkd.in/e48btBf4 🔍 Focus on data insights: - The gpt-4-1106-preview model achieved a 90% recall rate in detecting inconsistent claim pairs within a ternary assertions setup. - ChatGPT provided sound reasoning for the assertions between claims and hypotheses, based on an analysis grounded in relevance, specificity, and certainty. 💡 Main outcomes and implications: - ChatGPT's conclusions of consensus and controversies in clinical literature were comprehensive and factually consistent. - The research questions proposed by ChatGPT received high expert ratings. 📚 Field significance: - ChatGPT's capacity to evaluate and interpret scientific claims can be generalized to broader clinical research literature. - Caution is advised as ChatGPT's outputs are inferences drawn from the input literature and could be harmful to clinical practice. 🗄️: [#clinicalresearch, #scientificreasoning, #ChatGPT, #datainsights]

👉🏼 Developing large language models to detect adverse drug events in posts on X 🤓 Yu Deng 👇🏻 https://lnkd.in/emQta8y3 🔍 Focus on data insights: 🌐 Social media platforms like X

👉🏼 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

👉🏼 Generating synthetic clinical text with local large language models to identify misdiagnosed limb fractures in radiology reports 🤓 Jinghui Liu 👇🏻 https://lnkd.in/erGf4cF8 🔍 Focus on data insights: - 📊 The study shows that local, open-source LLMs can generate synthetic clinical reports effectively, achieving performance comparable to advanced models like ChatGPT and GPT-4. - 🤖 Synthetic data generated by local LLMs was able to assist in identifying misdiagnosed fractures, which is crucial for improving diagnostic accuracy. - 🧠 Utilizing synthetic data reduced the need for extensive real-world annotated datasets, streamlining the training process of downstream AI models. 💡 Main outcomes and implications: - 🎯 The findings indicate that local LLMs are a viable alternative for creating synthetic clinical reports, promoting data privacy and security. - 🔍 Synthetic reports alone achieved over 90% of the classification performance when compared to models trained exclusively on real-world data, suggesting robust utility. - 🌐 This approach may lower costs and enhance efficiency in AI training processes within healthcare settings. 📚 Field significance: - 🏥 The ability to generate high-quality synthetic clinical data can significantly contribute to the development of more efficient and effective diagnostic tools. - ⏳ This research may influence future practices in healthcare data management, especially in scenarios where patient privacy is paramount. - 🚀 Adoption of local LLMs could pave the way for advancements in medical AI applications, ensuring that innovations are both ethical and impactful. 🗄️: [#ArtificialIntelligence] [#Healthcare] [#SyntheticData] [#MachineLearning] [#ClinicalReports] [#Radiology] [#DataPrivacy]

👉🏼 Developing large language models to detect adverse drug events in posts on X 🤓 Yu Deng 👇🏻 https://lnkd.in/eVVYKA8b 🔍 Focus on data insights: - 📊 The study developed several 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]