Explainable and Trustworthy AI for Digital Health
Explainable and Trustworthy AI for Digital Health
We focus on interpretability, fairness, and reliability to build AI tools that clinicians and patients can trust. Our work includes explainable modelling, uncertainty quantification, and ethical AI design for safe deployment in real-world healthcare.
Publications
- Liu, Yu, et al. "SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis." Proceedings of the 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2025. paper, code
- Mesinovic, Munib, Peter Watkinson, and Tingting Zhu. "DySurv: dynamic deep learning model for survival analysis with conditional variational inference." Journal of the American Medical Informatics Association (2024): ocae271. paper
- Mesinovic, Munib, et al. "DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation." arXiv preprint arXiv:2503.22257 (2025). paper, code