Engineering FAIR Privacy-preserving Applications that Learn Histories of Disease
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915824284991488 |
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| author | Duarte, Ines N. Bhawsar, Praphulla M. S. Mason, Lee K. Balasubramanian, Jeya Balaji Russ, Daniel E. Oliveira, Arlindo L. Almeida, Jonas S. |
| author_facet | Duarte, Ines N. Bhawsar, Praphulla M. S. Mason, Lee K. Balasubramanian, Jeya Balaji Russ, Daniel E. Oliveira, Arlindo L. Almeida, Jonas S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00181 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Engineering FAIR Privacy-preserving Applications that Learn Histories of Disease Duarte, Ines N. Bhawsar, Praphulla M. S. Mason, Lee K. Balasubramanian, Jeya Balaji Russ, Daniel E. Oliveira, Arlindo L. Almeida, Jonas S. Machine Learning Artificial Intelligence Software Engineering 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. |
| title | Engineering FAIR Privacy-preserving Applications that Learn Histories of Disease |
| topic | Machine Learning Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2603.00181 |