Researchers used causal machine learning to model time-dependent treatment effects of radiotherapy and chemotherapy in lower-grade gliomas, according to the report summarized as an AI-driven approach to “treatment timelines.” The work aims to map how outcomes change over time rather than treating response as a single snapshot. For clinicians and trial designers, the focus on causal, time-linked effects targets a core challenge in glioma management: disentangling how different treatment courses influence disease evolution. The study frames AI as a tool for extracting temporal dynamics from complex treatment histories.