Multimodal AI Outperforms Single Biomarkers for Lung Cancer Treatment Prediction

Multimodal AI integrating imaging, pathology, and genomics outperforms unimodal biomarkers for treatment prediction in lung cancer, according to a review of technical architectures and clinical studies.

• Multimodal AI combines radiologic imaging, digital pathology, genomics, and other sources to overcome PD-L1 single-biomarker limitations.
• Key architectures include convolutional neural networks, vision transformers, and graph neural networks using cross-attention fusion.
• Clinical applications include treatment selection, ctDNA monitoring, and variant interpretation.

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