Reconstructing individual patient data (IPD) from Kaplan-Meier (KM) plots provides valuable insights for evidence synthesis in clinical research. However, existing approaches often rely on manual digitization, which is error-prone and lacks scalability. To address these limitations, we develop KM-GPT, the first fully automated, AI-powered pipeline for reconstructing IPD directly from KM plots with high accuracy, robustness, and reproducibility. KM-GPT integrates advanced image preprocessing, multi-modal reasoning powered by GPT-5, and iterative reconstruction algorithms to generate high-quality IPD without manual input or intervention. Its hybrid reasoning architecture automates the conversion of unstructured information into structured data flows and validates data extraction from complex KM plots. To improve accessibility, KM-GPT is equipped with a user-friendly web interface and an integrated AI assistant, enabling researchers to reconstruct IPD without requiring programming expertise. KM-GPT was rigorously evaluated on synthetic and real-world datasets, consistently demonstrating superior accuracy. To illustrate its utility, we applied KM-GPT to a meta-analysis of gastric cancer immunotherapy trials, reconstructing IPD to facilitate evidence synthesis and biomarker-based subgroup analyses. By automating traditionally manual processes and providing a scalable, web-based solution, KM-GPT transforms clinical research by leveraging reconstructed IPD to enable more informed downstream analyses, supporting evidence-based decision-making.

Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance.