Federated and Generative AI Models for Secure, Cross-Institutional Healthcare Data Interoperability
Keywords:
Terms—Federated Learning, Generative AI, Cross- Institutional Healthcare Interoperability, Data Safety, Seman- tic Interoperability, Evaluation Matrices, Implementation Play- booksAbstract
In a clinical world filled with siloed information, AI models can be employed that require only the exchange of the learned model parameters instead of the raw data itself; preserving the privacy of local patients without any manual copying and pasting. Synthetically generated data, produced using AI models that have been trained with a privacy-by-design philosophy, can also be allowed into real clinical use. The medical community can therefore benefit from additional data created with Gardens of Trust and operated under Machine Learning as a Service paradigms; without coining new terms or practicing new technologies. What is still missing for a truly symbiotic ecosystem picturing a connected and collaborative cross-institutional clinical AI without renaming normality is the completion of a cross- institutional interoperability step. A step in which different AI stakeholders participating in the plot of the story and willing to exchange their locally generated knowledge, are conversing into a common language allowing the understanding of the same data semantics. Generating semantic-rich data is the first building block to undertake such a step until now. Semantic standards and ontologies expressed on the data are guiding the data pipeline in a way that data requests and responses follow a clearly defined schema in a semantic-wide way. . . even if the generative act is not trusted. Identification of trust in use and data provenance are the guardians of all plots: users play a crucial role in understanding whether content is useful or harmful, with a disentangled posture allowing the evaluation of data provenance along the path. Yet, an adoption threat model helps building the narrative documenting the dangers underlying the technology in a more complete way, covering high-level clinical applications up to low-level magic tricks.
Downloads
References
Singireddy, J. (2024). Deep Learning Architectures for Automated Fraud Detection in Payroll and Financial Management Services: Towards Safer Small Business Transactions. Journal of Artificial Intelligence and Big Data Disciplines, 1(1), 75-85.
Bae, J., Kim, H., & Park, S. (2024). Federated learning for privacy- preserving clinical analytics across hospital networks. Nature Medicine, 30(1), 112–120. https://doi.org/10.1038/s41591-023-02689-1
Zhang, Y., Huang, Z., & Xu, X. (2024). Secure federated clinical modeling using differential privacy in heterogeneous EHR systems. IEEE Journal of Biomedical and Health Informatics, 28(2), 745–757. https://doi.org/10.1109/JBHI.2024.3341122
Sheelam, G. K. (2024). AI-Driven Spectrum Management: Using Ma- chine Learning and Agentic Intelligence for Dynamic Wireless Op- timization. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN 3050-9734 en e-ISSN 3050-9742, 2(1).
Huang, Z., Xu, X., Liu, R., Jiang, W., & Fu, Z. (2022). A survey on privacy-preserving machine learning for healthcare. ACM Computing Surveys, 55(8), 1–36. https://doi.org/10.1145/3527151
Kumar, S., Patel, V., & Lee, J. (2024). Interoperable federated architectures for multi-institutional health data exchange. Journal of the American Medical Informatics Association, 31(3), 412–423. https://doi.org/10.1093/jamia/ocad313
Nandan, B. P. (2024). Revolutionizing Semiconductor Chip Design through Generative AI and Reinforcement Learning: A Novel Approach to Mask Patterning and Resolution Enhancement. International Journal of Medical Toxicology and Legal Medicine, 27(5), 759-772.
Rasouli, M., Chen, R. J., & Mahmood, F. (2024). Synthetic data governance in healthcare AI: A review of safeguards and failure modes. Nature Digital Medicine, 7(1), 19. https://doi.org/10.1038/s41746-024-
00923-3
Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F., & Mah- mood, F. (2021). Synthetic data in machine learning for medicine and healthcare. Nature Biomedical Engineering, 5(6), 493–497. https://doi.org/10.1038/s41551-021-00751-8
Pandiri, L., & Chitta, S. (2024). Machine Learning-Powered Actuarial Science: Revolutionizing Underwriting and Policy Pricing for Enhanced Predictive Analytics in Life and Health Insurance.
Huang, G., Liu, Y., & Chen, Z. (2024). Secure cross-border data inter- operability using federated identity and privacy-preserving computation. IEEE Transactions on Information Forensics and Security, 19, 441–455. https://doi.org/10.1109/TIFS.2023.3332011
Blandfort, P., Fauw, J. D., & Kohli, P. (2024). Evaluating trustwor- thiness in clinical generative models. NPJ Digital Medicine, 7(2), 31. https://doi.org/10.1038/s41746-024-00942-0
Meda, R. (2024). Predictive Maintenance of Spray Equipment Using Machine Learning in Paint Application Services. European Data Science Journal (EDSJ) p-ISSN 3050-9572 en e-ISSN 3050-9580, 2(1).
Li, Q., Yang, F., & Tan, J. (2024). Federated multimodal learning for diagnostic imaging across distributed hospital systems. Medical Image Analysis, 94, 103125. https://doi.org/10.1016/j.media.2024.103125
Bourquard, A., Maier, A., & Rueckert, D. (2024). Privacy- preserving medical AI: Emerging standards and interoperabil- ity frameworks. Artificial Intelligence in Medicine, 146, 102789. https://doi.org/10.1016/j.artmed.2023.102789
Somu, B. (2024). Agentic AI and Financial Compliance: Autonomous Systems for Regulatory Monitoring in Banking. European Data Science Journal (EDSJ) p-ISSN 3050-9572 en e-ISSN 3050-9580, 2(1).
ang, Z., Wu, M., & Shen, D. (2024). Benchmarking federated learning algorithms for clinical image classification. IEEE Transactions on Med- ical Imaging, 43(1), 88–101. https://doi.org/10.1109/TMI.2023.3328420
Gopinath, K., Hou, L., & Morris, Q. (2024). Harmonizing heterogeneous medical ontologies using foundation models. Nature Communications, 15, 2009. https://doi.org/10.1038/s41467-024-41945-9
Inala, R., & Somu, B. (2024). Agentic AI in Retail Banking: Redefining Customer Service and Financial Decision-Making. Journal of Artificial Intelligence and Big Data Disciplines, 1(1).
Perry, A., Azizi, S., & Lungren, M. (2024). Language-model-guided evaluation pipelines for clinical safety and toxicity in synthetic data. Lancet Digital Health, 6(1), e25–e36. https://doi.org/10.1016/S2589- 7500(23)00224-1
Zhou, Y., Chen, S., & Wang, F. (2024). Detecting member- ship inference attacks on federated medical models. IEEE Trans- actions on Dependable and Secure Computing, 21(2), 256–270. https://doi.org/10.1109/TDSC.2023.3320154
Motamary, S. (2024). Data Engineering Strategies for Scaling AI-Driven OSS/BSS Platforms in Retail Manufacturing. BSS Platforms in Retail Manufacturing(December 10, 2024).
Badar, R., Jha, D., & Tschannen, M. (2024). Foundation models for interoperable medical data semantics across institutions. Patterns, 5(1), 100978. https://doi.org/10.1016/j.patter.2023.100978
Sun, H., Xu, W., & Li, J. (2024). Multicenter federated training with adaptive privacy budgets in health data ecosystems. IEEE Access, 12, 10231–10245. https://doi.org/10.1109/ACCESS.2024.3365524
Lakkarasu, P. (2024). From Model to Value: Engineering End-to-End AI Systems with Scalable Data Infrastructure and Continuous ML Delivery. European Journal of Analytics and Artificial Intelligence (EJAAI) p- ISSN 3050-9556 en e-ISSN 3050-9564, 2(1).
Chung, H., Ravi, N., & Ghaffari, M. (2024). Protecting sensitive patient attributes via adversarially-trained generative models. Machine Learning for Healthcare, 12(1), 1–19. https://doi.org/10.1145/3639278
Mao, Y., Zhou, X., & Fan, J. (2024). Threat modeling for federated healthcare pipelines using STRIDE+. Computers & Security, 139, 103010. https://doi.org/10.1016/j.cose.2023.103010
Hameed, S., Zhang, T., & Albarqouni, S. (2024). Practical deploy- ment pathways for federated AI in hospitals: Lessons from pilot to practice. Health Information Science and Systems, 12(1), 14. https://doi.org/10.1007/s13755-024-00252-7
Kruse, C., Glick, G., & Stewart, L. (2024). Interoperability and semantic harmonization for AI-driven clinical systems. International Journal of Medical Informatics, 184, 105413. https://doi.org/10.1016/j.ijmedinf.2023.105413
Shao, Z., Feng, Z., & Lu, Y. (2024). Evaluating safety, utility, and calibration of generative medical imaging models. Radiology: Artificial Intelligence, 6(2), e220409. https://doi.org/10.1148/ryai.220409
Luo, Y., Chen, P., & Zhang, J. (2024). Privacy-enhanced federated medical analytics using secure aggregation and adaptive noise injection. IEEE Transactions on Neural Networks and Learning Systems, 35(4), 512–525. https://doi.org/10.1109/TNNLS.2023.3338129
Rao, A., Choi, J., & Sun, L. (2024). Synthetic EHR generation with transformer-based diffusion models for clinical research. Journal of Biomedical Informatics, 152, 104502. https://doi.org/10.1016/j.jbi.2024.104502
Gao, X., Mitchell, R., & Li, K. (2024). Federated foundation models for cross-institutional healthcare interoperability. IEEE Transactions on Big Data, 10(1), 77–92. https://doi.org/10.1109/TBDATA.2023.3340028
Velicˇkovic´, P., Neil, M., & Esmaili, N. (2024). Trust assessments for clinical generative AI systems: A multi-metric evaluation framework. Nature Communications, 15, 3201. https://doi.org/10.1038/s41467-024-43107-3.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
