A review of AI-driven single-omics, multimodal, and multi-omics studies in Breast Cancer found that these tools can predict neoadjuvant chemotherapy response, identify candidate biomarkers, and characterize tumor heterogeneity and the immune microenvironment. The paper also highlights newer spatial multi-omics and large language model approaches, while noting that data quality, interpretability, ethics, and clinical translation remain major barriers.

Why It Matters To Your Practice

• AI-based multimodal integration may improve pre-treatment stratification for patients being considered for neoadjuvant chemotherapy.

• Better biomarker discovery could help identify which tumors are more likely to respond, potentially reducing ineffective treatment.

• Profiling heterogeneity and the immune microenvironment may support more individualized treatment planning.

Clinical Implications

• Clinicians should expect growing use of combined pathology, imaging, genomic, transcriptomic, and spatial data in treatment-response modeling.

• Current evidence is promising but largely preparatory for implementation rather than practice-changing on its own.

• Interpretability, validation across populations, and workflow integration will be essential before routine clinical adoption.

Insights

• The review argues that AI is most useful when integrating high-dimensional data rather than relying on any single omics layer.

• Emerging spatial multi-omics may better decode intratumoral heterogeneity relevant to neoadjuvant chemotherapy response.

• Large language models are discussed as potential decision-support tools, though their clinical role remains early and needs guardrails.

The Bottom Line

• Multimodal AI is a promising route to more precise neoadjuvant chemotherapy selection in breast cancer, especially through biomarker discovery and response prediction.

• For now, the main takeaway for practice is to watch for validated tools rather than adopt unproven models.

• Clinical impact will depend on robust data-sharing, transparent models, and successful prospective translation.