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. Author manuscript; available in PMC: 2023 Nov 17.
Published in final edited form as: Cancer Cell. 2022 Oct 10;40(10):1095–1110. doi: 10.1016/j.ccell.2022.09.012

Figure 1. AI-driven multimodal data integration.

Figure 1.

A) AI models can integrate complemenatary information and clinical context from diverse data sources to provide more accurate outcome predictions. The clinical insights identified by such models can be further elucidated through C) interpretability methods and D) quantitative analysis to guide and accelerate the discovery of new biomarkers or therapeutic targets (E-F). B) AI can reveal novel multimodal interconnestions, such as relation between certain mutations and changes in cellular morphology or associations between radiology findings and histology tumor subtypes or molecular features. Such associations can serve as non-invasive or cost-efficient alternatives to existing biomarkers to support large-scale patient screening (E-F)