An Interactive Trustworthy AI Pathology Copilot to Improve Biomarker-Driven Prognostic Stratification and Therapeutic Response Prediction

Predictive assays for precision oncology increasingly rely on multi-scale biomarkers that manifest as morphologic signatures in routine whole-slide images (WSIs). However, most computational pathology models treat biomarker profiling and outcome prediction (i.e., prognostic stratification and therapeutic response) as independent tasks, and lack the interactive and trustworthy capabilities required for clinical translation. Here, we present TEAM, an interactive trustworthy AI pathology copilot that improves biomarker-driven outcome prediction. Pretrained on 55,648 pan-cancer WSIs and 1,750,648 regions of interest (ROIs), comprising 360 million patches, TEAM learns risk-aware embeddings by conditioning on clinical metadata and aligning with relative risk prior. For trustworthy assessment, TEAM quantifies patch-level data (aleatoric) and model (epistemic) uncertainty, then propagates these estimates to patient-level predictions. In outcome prediction, profiled biomarkers serve as intermediate features to contextualize prognostic and therapeutic estimates. Beyond passive prediction, TEAM integrates vision-language models with agentic orchestration for clinical reasoning, and provides a web-based clinician-in-the-loop interface for interactive prediction refinement. Evaluated across 48 multi-institutional cohorts encompassing 85 benchmarks, TEAM consistently outperforms existing methods across biomarker profiling, prognostic stratification, and therapeutic response prediction, supporting trustworthy AI-assisted decision-making in computational pathology.