工程实现符合FAIR原则的隐私保护应用：学习疾病自然史
Engineering FAIR Privacy-preserving Applications that Learn Histories of Disease
AI总结：
本研究通过浏览器内部署生成式AI模型，实现符合FAIR原则的隐私保护应用，用于学习疾病自然史并提升医疗领域隐私保护性能。
摘要：
最近关于'利用生成转换器学习人类疾病自然史'的报告创造了一个机会，评估在隐私敏感领域交付面向用户的生成式AI应用的工程挑战。这些模型的应用，特别是用于个性化医疗任务如预测个体患病风险，通常受到数据隐私问题的限制。因此，该项目被设计为一个浏览器内的模型部署练习（一个'应用'），测试客户端端推断生成的架构边界（无下载或安装）。我们仅依赖参考报告中的文档来开发模型，特别是测试FAIR数据原则中的'R'组件：可发现性、可访问性、互操作性和可重用性。成功的模型部署，利用ONNX和自定义JavaScript SDK，建立了一个安全、高性能的架构蓝图，为未来医学中的隐私生成AI奠定基础。
英文摘要：
A recent report on "Learning the natural history of human disease with generative transformers" created an opportunity to assess the engineering challenge of delivering user-facing Generative AI applications in privacy-sensitive domains. The application of these models, particularly for personalized healthcare tasks like predicting individual morbidity risk, is typically constrained by data privacy concerns. This project was accordingly designed as an in-browser model deployment exercise (an "App") testing the architectural boundaries of client-side inference generation (no downloads or installations). We relied exclusively on the documentation provided in the reference report to develop the model, specifically testing the "R" component of the FAIR data principles: Findability, Accessibility, Interoperability, and Reusability. The successful model deployment, leveraging ONNX and a custom JavaScript SDK, establishes a secure, high-performance architectural blueprint for the future of private generative AI in medicine.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
