Multimodal AI data integration in precision oncology

Multimodal AI fusion of imaging, pathology, clinical records, and omics data can improve diagnostic sensitivity, tumor grading, and prediction of immunotherapy response and survival, but adoption is limited by fragmentation, bias, explainability, and regulation.

• Teaches how early, late, intermediate, and hybrid fusion strategies combine radiology, histopathology, clinical records, and multi-omics to bridge genotype–phenotype gaps.
• Audience: radiologists, trainees, and oncology imaging clinicians seeking a practical overview of multimodal AI frameworks.
• Take-home: multimodal AI improves diagnostic sensitivity, tumor grading, and immunotherapy response/survival prediction; translation requires addressing fragmentation, bias, explainability, and regulation.

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