Journal Article

Achieving inclusive healthcare through integrating education and research with AI and personalized curricula

Miller, Alison Derbenwick, Lai, Jaslene, Akhavan-Sarraf, Ramin, Goncalves, Francesca, Li-Pook-Than, Jennifer, Wang, Meng, Monte, Emma, Alavi, Arash, Dixit, Amit, McGhee, Eva M., Akbari, Ilya, Schüssler-Fiorenza Rose, Sophia Miryam, Wu, Joseph C., Kundaje, Anshul, Edmiston, Scott, Tondar, Abtin, Saxman, Paul, Cha, Kexin, Rodriguez, David Jose Florez, Yracheta, Joseph, Babu, Mohan, Yegnashankaran, Kritika, Zhou, Xin, Snyder, Michael, Ross, Antony, Nebeker, Camille, Park, Ryan, Zhang, Xinyue, Contrepois, Kévin, Ma, Shirley, Bahmani, Amir, Dale, Kali, Nair, Ramesh

2025

Overview

Background Precision medicine promises significant health benefits but faces challenges such as 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) such as Generative Pretrained Transformer (GPT) and Claude highlights the importance of making complex data accessible to non-specialists. Methods We evaluated the Stanford Data Ocean (SDO) precision medicine training program’s learning outcomes, AI Tutor performance, and learner satisfaction by assessing self-rated competency on key learning objectives through pre- and post-learning surveys, along with formative and summative assessment completion rates. We also analyzed AI Tutor accuracy and learners’ self-reported satisfaction, and post-program academic and career impacts. Additionally, we demonstrated the capabilities of the AI Data Visualization tool. Results SDO demonstrates the ability to improve learning outcomes for learners from broad educational and socioeconomic backgrounds with the support of the AI Tutor. The AI Data Visualization tool enables learners to interpret multi-omics and wearable data and replicate research findings. Conclusions SDO strives to mitigate challenges in precision medicine through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning. SDO provides AI Tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible to users from broad 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. Bahmani, Cha, Alavi, Dixit et al. evaluate an AI-facilitated precision medicine learning platform they built, Stanford Data Ocean. The platform, which provided 3594 costfree certification accesses across 93 countries, demonstrates positive training outcomes across bioinformatics topics for low and middle income learners. Plain language summary Precision medicine is the use of various types of health data specific to an individual to improve disease prevention, diagnosis, or treatment. We used artificial intelligence to build a precision medicine learning platform for clinicians and researchers in training. Students in 93 countries accessed the platform and found it helpful. It could be particularly helpful for training students in low- and middle-income countries.

• Publisher: Nature Publishing Group UK,Springer Nature B.V,Nature Portfolio
• Subject: 631/114 / 692/700/478 / Artificial intelligence / Bioinformatics / Community / Consortia / Curricula / Data visualization / Datasets / Education / Genomes / Large language models / Learning / Medicine / Medicine & Public Health / Personal health / Precision medicine / Privacy / Sustainable development