Deep Survival Analysis: A Comprehensive Review of Methods, Applications, and Open Challenges in Biomedicine

Survival analysis is fundamental to biomedical research and clinical decision-making, where outcomes such as mortality, disease progression, relapse, and time-to-discharge are frequently observed under censoring and competing risks. Recent advances in deep learning have expanded the capabilities of survival modeling by enabling flexible nonlinear representations of heterogeneous biomedical data, including electronic health records, medical imaging, and multi-omics profiles. This structured narrative review synthesizes studies published between 2018 and 2026, examining major deep survival model families, including neural extensions of the Cox proportional hazards model, discrete-time and distribution-based approaches, multimodal architectures, transformers, and emerging foundation-model paradigms. We critically compare deep learning approaches with classical and machine learning survival methods, highlighting the conditions under which increased model complexity provides meaningful advantages. We further examine representative biomedical applications, datasets, evaluation and benchmarking practices, with particular emphasis on calibration, competing risks, external validation, and model selection. The review identifies persistent challenges in generalization, sample size and event scarcity, censoring mechanisms, interpretability, fairness, reproducibility, and clinical translation, while outlining emerging directions toward robust, trustworthy, and clinically actionable survival modeling systems.