Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy

Neoadjuvant therapy for breast cancer is planned by subtype: pathologic complete response (pCR) differs in frequency, meaning, and surrogate validity across HR+/HER2-, HER2+, and triple-negative breast cancer (TNBC). This review examines whether current evidence supports using artificial intelligence (AI) to move beyond predicting response under a fixed regimen to guiding systemic treatment tailoring. Here, tailoring means model-guided drug omission, switching, escalation, or de-escalation for an individual patient. We appraised 102 full-text studies of AI-based response prediction (2020-2026), organized by subtype and clinical decision, and graded each on an author-defined five-level clinical-readiness ladder (L1-L5) measuring validation and translational maturity rather than accuracy. Risk of bias was assessed with PROBAST and reporting against TRIPOD+AI. Readiness clustered low: 43 studies reached internal validation only (L1), 53 temporal or geographic external validation (L2), and 6 prospective observational validation (L3); none reached workflow integration (L4) or interventional evidence (L5). Most carried high overall risk of bias (89/102); external validation appeared in 52 (51%), fully reported decision-curve analysis in 44 (43%), and calibration in 19 (19%). Strategy comparison and individualized-treatment-effect analyses were essentially absent. Subtype-specific AI currently predicts response under fixed regimens with moderate-to-good discrimination; the evidence does not yet support changing an individual patient's systemic treatment.