Amir Bahmani, Kexin Cha, Arash Alavi, Amit Dixit, Antony Ross, Ryan Park, Francesca Goncalves, Shirley Ma, Paul Saxman, Ramesh Nair, Ramin Akhavan Sarraf, Xin Zhou, Meng Wang, Kevin Contrepois, Jennifer Li Pook Than, Emma Monte, David Jose Florez Rodriguez, Jaslene Lai, Mohan Babu, Abtin Tondar, Sophia Miryam Schussler-Fiorenza Rose, Ilya Akbari, Xinyue Zhang, Kritika Yegnashankaran, Joseph Yracheta, Kali Dale, Alison Derbenwick Miller, Scott Edmiston, Eva M McGhee, Camille Nebeker, Joseph C Wu, Anshul Kundaje, Michael Snyder
{"title":"通过将教育和研究与人工智能和个性化课程相结合,实现包容性医疗保健","authors":"Amir Bahmani, Kexin Cha, Arash Alavi, Amit Dixit, Antony Ross, Ryan Park, Francesca Goncalves, Shirley Ma, Paul Saxman, Ramesh Nair, Ramin Akhavan Sarraf, Xin Zhou, Meng Wang, Kevin Contrepois, Jennifer Li Pook Than, Emma Monte, David Jose Florez Rodriguez, Jaslene Lai, Mohan Babu, Abtin Tondar, Sophia Miryam Schussler-Fiorenza Rose, Ilya Akbari, Xinyue Zhang, Kritika Yegnashankaran, Joseph Yracheta, Kali Dale, Alison Derbenwick Miller, Scott Edmiston, Eva M McGhee, Camille Nebeker, Joseph C Wu, Anshul Kundaje, Michael Snyder","doi":"10.1101/2024.07.31.24311182","DOIUrl":null,"url":null,"abstract":"Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.","PeriodicalId":501454,"journal":{"name":"medRxiv - Health Informatics","volume":"86 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula\",\"authors\":\"Amir Bahmani, Kexin Cha, Arash Alavi, Amit Dixit, Antony Ross, Ryan Park, Francesca Goncalves, Shirley Ma, Paul Saxman, Ramesh Nair, Ramin Akhavan Sarraf, Xin Zhou, Meng Wang, Kevin Contrepois, Jennifer Li Pook Than, Emma Monte, David Jose Florez Rodriguez, Jaslene Lai, Mohan Babu, Abtin Tondar, Sophia Miryam Schussler-Fiorenza Rose, Ilya Akbari, Xinyue Zhang, Kritika Yegnashankaran, Joseph Yracheta, Kali Dale, Alison Derbenwick Miller, Scott Edmiston, Eva M McGhee, Camille Nebeker, Joseph C Wu, Anshul Kundaje, Michael Snyder\",\"doi\":\"10.1101/2024.07.31.24311182\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.\",\"PeriodicalId\":501454,\"journal\":{\"name\":\"medRxiv - Health Informatics\",\"volume\":\"86 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv - Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.07.31.24311182\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.07.31.24311182","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
精准医疗有望带来巨大的健康效益,但也面临着各种挑战,例如需要复杂的数据管理和分析、跨学科合作以及对研究人员、医疗保健专业人员和参与者的教育。要满足这些需求,就必须整合计算专家、工程师、设计师和医疗保健专业人员,开发用户友好型系统和共享术语。GPT-4 和 Claude 3 等大型语言模型(LLM)的广泛采用凸显了让非专业人员也能访问复杂数据的重要性。斯坦福数据海洋(SDO)致力于通过一个可扩展的云平台来缓解这些挑战,该平台支持各种数据类型的数据管理、高级研究和精准医学中的个性化学习。SDO 提供人工智能辅导员和人工智能驱动的数据可视化工具,以提高教育和研究成果,使来自不同教育背景的用户都能进行数据分析。通过在全球范围内扩大参与和尖端研究能力,SDO 尤其惠及经济上处于不利地位和历史上被边缘化的社区,促进跨学科生物医学研究,缩小生物医学领域教育与实际应用之间的差距。
Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula
Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.