AI Decodes the Data Deluge to Personalise Cancer Care

Cancer is characterised by staggering molecular heterogeneity, meaning traditional methods that analyse a single biological layer often fall short. To improve diagnostic accuracy, scientists are turning to multi-omics—the integration of data spanning genomics, transcriptomics, proteomics, metabolomics, and radiomics. However, combining these massive, disparate datasets requires immense computational power.

This is where artificial intelligence steps in. By utilising deep learning and machine learning, specifically techniques like graph neural networks for biological modelling and transformers for cross-modal fusion, researchers can translate complex data into actionable clinical insights. Recent integrated classifiers have already reported promising accuracy scores (AUCs of 0.81–0.87) for challenging early-detection tasks.

The review highlights critical applications, including AI-driven therapy selection to predict drug resistance and radiogenomic non-invasive diagnostics. While challenges such as data harmonisation and ethical equity remain, emerging trends like privacy-preserving federated learning and patient-centric 'N-of-1' models are paving the way for a major paradigm shift. This approach promises to transform oncology, moving beyond population-based averages to truly dynamic, personalised management.