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AI is transforming cardio-oncology by integrating multi-omics, imaging, and biosensor data for early risk stratification of cardiovascular toxicity.
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AI-driven models enable continuous predictive refinement of patient-specific toxicity trajectories, surpassing traditional static risk assessments.
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3
The integration of multi-omics data reveals shared molecular mechanisms between cancer therapies and cardiovascular disease, enhancing mechanistic understanding.
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4
AI facilitates the identification of dual-purpose agents through drug repurposing frameworks, targeting both cancer and cardiotoxicity.
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Challenges in AI implementation include data heterogeneity and model interpretability, but solutions like federated learning offer pathways for clinical translation.
