AI Streamlines Prostate Pathology Data Extraction

AbstractManual extraction of pathology data from electronic health records remains a labor-intensive barrier to large-scale clinical research. In this editorial, we discuss the findings of Azar et al., who demonstrate that a large language model can accurately extract structured data from radical prostatectomy pathology reports, achieving near-perfect concordance with human reviewers. This work highlights the feasibility of deploying AI for high-fidelity data abstraction in oncology research and outlines the potential for broader integration into institutional databases and cancer registries. While limitations in generalizability remain, the results signal a transformative step toward scalable, automated research data curation.