Democratizing and accelerating AI-driven pathology research through agentic intelligence
Computational pathology has advanced rapidly with the emergence of foundation models, yet widespread adoption remains limited by substantial technical complexity and programming requirements. Here we present PathLab, an autonomous agentic framework that translates natural-language research objectives into executable and validated computational pathology workflows through the structured composition of domain-specific skills and tools. By organizing workflow generation around reusable methodological modules, including data preprocessing, model development, evaluation and interpretation, PathLab enables studies to be specified at the level of scientific intent rather than implementation deta...
| Authors | Jiabo Ma, Cheng Jin, Yihui Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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