Self-Supervised Physiological Foundation Models for Early Risk Prediction and Secure Human–AI Collaboration in Healthcare
Keywords:
physiological foundation models; self-supervised learning; early risk prediction; human-AI collaboration; adversarial robustness; federated learning; governanceAbstract
The transformation of physiological monitoring from episodic clinical encounters to continuous, multimodal data streams opens unprecedented opportunities for early risk prediction and personalized intervention. Self-supervised physiological foundation models, pretrained on vast corpora of electrocardiogram, photoplethysmogram, electroencephalogram, and related biosignals, are poised to become the backbone of next-generation health intelligence systems. This paper presents a system-level analysis of the architectural paradigms, deployment architectures, and governance frameworks necessary to translate such models into trustworthy, secure human–AI collaborations. We examine how contrastive, generative, and masked-reconstruction objectives can capture latent physiological dynamics without costly manual annotations, enabling early detection of cardiovascular, neurological, and metabolic perturbations months before clinical manifestation. The discussion extends beyond model accuracy to encompass the full sociotechnical stack: federated and split learning infrastructures that preserve privacy, edge-cloud orchestration for real-time inference, adversarial resilience against data poisoning and evasion attacks, and continuous monitoring of fairness and calibration drift across heterogeneous populations. Special attention is devoted to the integration of large language model agents in clinical decision pipelines, where secure human-AI collaboration requires adversarial robustness guarantees and transparent uncertainty communication. Through cross-domain comparisons with imaging and natural language foundation models and forward-looking policy analysis, we argue that sustainable, equitable deployment demands institutional mechanisms for auditability, dynamic consent, and algorithmic stewardship. The paper concludes by outlining a research roadmap that couples self-supervised representation learning with system-level safeguards to achieve early risk prediction without compromising patient autonomy or clinical safety.
References
1. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186). Association for Computational Linguistics.
2. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (pp. 1597–1607). PMLR.
3. Banville, H., Chehab, O., Hyvärinen, A., Engemann, D.-A., & Gramfort, A. (2021). Uncovering the structure of clinical EEG signals with self-supervised learning. Journal of Neural Engineering, 18(4), 046020.
4. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16000–16009). IEEE.
5. Kiyasseh, D., Zhu, T., & Clifton, D. A. (2021). CLOCS: Contrastive learning of cardiac signals across space, time, and patients. In Advances in Neural Information Processing Systems (NeurIPS 2021).
6. Hu, S. (2026). Research on Security Enhancement Methods for Adversarial Robust Large Language Model Intelligent Agents for Medical Decision-Making Tasks. arXiv preprint arXiv:2605.08257.
7. Guo, Z., Chen, T., Jiao, Y., Pan, Y., Hu, X., & Ferrario, M. (2026). SIGMA-PPG: Statistical-prior Informed Generative Masking Architecture for PPG Foundation Model. arXiv preprint arXiv:2601.21031.
8. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (pp. 8748–8763). PMLR.
9. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, 119.
10. Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., & Kohane, I. S. (2019). Adversarial attacks on medical machine learning. Science, 363(6433), 1287–1289.
11. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
12. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (NeurIPS 2017).
13. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (pp. 308–318). ACM.
14. Chen, M., Hao, Y., Li, Y., Lai, C.-F., & Wu, D. (2018). Edge computing for real-time health monitoring: A survey. IEEE Communications Surveys & Tutorials, 20(4), 2915–2948.
15. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics.
16. U.S. Food and Drug Administration, Health Canada, & Medicines and Healthcare products Regulatory Agency. (2021). Good Machine Learning Practice for Medical Device Development: Guiding Principles.
17. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
18. Moor, M., Banerjee, M., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956), 259–265.
19. Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In International Conference on Machine Learning (pp. 1050–1059). PMLR.
20. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71.
21. Yoon, J., Jarrett, D., & van der Schaar, M. (2019). Time-series generative adversarial networks. In Advances in Neural Information Processing Systems (NeurIPS 2019).
22. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1175–1191). ACM.
23. Kiyasseh, D., Tadesse, G. A., Nguyen, T. N. T., Zhu, T., & Clifton, D. A. (2021). A clinical report of external generalisation from a deep learning model for cardiac arrhythmias. Nature Machine Intelligence, 3(9), 759–767.
24. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). ACM.
25. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
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