Overview

AI fuses imaging and histopathology data to correlate radiologic features with microscopic findings. This multimodal approach enhances diagnostic accuracy and understanding of disease biology. It supports precision diagnostics and research.

Methodology

Co registration and joint modeling align imaging scales and features. Cross modal representations enable prediction of histologic patterns from imaging. Large annotated datasets enable robust model training.

Clinical Use

Fusion aids in non invasive prediction of tumor grade and margins. It supports targeted biopsies and personalized therapy planning. Multidisciplinary workflows integrate findings for comprehensive care.

Challenges

Data alignment and differing spatial scales complicate fusion. Standardized labeling and cross discipline collaboration are essential. Validation across cohorts ensures generalizability.