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":"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}
引用次数: 0
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.