在浏览器内部署生成式AI，保护隐私地预测疾病发展史
Engineering FAIR Privacy-preserving Applications that Learn Histories of Disease
- 模型在浏览器端运行，无需下载安装
- 基于ONNX与自研JS SDK实现高安全性能部署
- 为医疗生成式AI提供可复用的隐私保护架构
一篇关于「用生成式变换器学习人类疾病自然史」的报告，揭示了在隐私敏感领域部署面向用户的生成式AI所面临的工程挑战。此类模型应用于个性化医疗任务（如预测个体患病风险）时，常受数据隐私顾虑制约。本项目作为浏览器内模型部署实验（即「应用」），旨在测试客户端推理生成的架构边界（无下载或安装）。我们完全依据参考报告的文档开发模型，重点验证了FAIR数据原则中的「可重用性」（Reusability）。通过ONNX与定制化JavaScript SDK成功实现模型部署，建立了一个安全、高性能的私有生成式AI医疗应用架构蓝图。
原文摘要 · Abstract (English)
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.
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