Artificial intelligence (AI) offers transformative potential in pathology, where histopathological images remain the diagnostic gold standard due to their rich morphological and molecular information. While the rapid development of AI-driven computational pathology tools is revolutionizing disease interpretation, these technologies have not yet been systematically evaluated. Therefore, this review systematically evaluates AI applications across the diagnostic continuum, from image preprocessing and tumor classification to prognostic stratification and the discovery of predictive biomarkers. It presents a technical taxonomy of the algorithms and foundation models powering these applications, benchmarking their performance across diverse diagnostic tasks through rigorous comparative analyses. It also identifies critical challenges in clinical translation, including computational scaling, noisy annotations, interpretability gaps, and domain shifts. Finally, it proposes a roadmap for advancing AI applications in precision oncology and pathological research. By bridging technological innovation with clinical needs, this review aims to accelerate the integration of robust, unified, scalable AI solutions into diagnostic workflows.

Background: Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data.Methods: MRI‐based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis.Results: The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin‐like growth factor binding, protein localization to membranes, and cytoskeleton‐dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05).Conclusions: Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.

Accurate prediction of pathological complete response (pCR) to neoadjuvant therapy in luminal breast cancer remains challenging, hindering precise treatment decisions. Here, we present the Lumi-Guide system, a novel multimodal framework that integrates deep-learning-based MRI analysis with clinical and genomic data to enable personalized treatment selection. In this multicenter study, we analyzed 1,097 patients with luminal breast cancer from 6 international datasets. We developed a Swin Transformer-based Lumi-I model using 3-plane dynamic contrast-enhanced MRI data and integrated it with clinical factors to construct the Lumi-CI model, which demonstrated robust performance across the validation and 2 external test sets, with areas under the receiver operating characteristic curves of 0.810, 0.819, and 0.864, respectively. Radiogenomic analysis revealed distinct biological characteristics: The Lumi-I high-score group exhibited immunologically active and proliferative microenvironments, while the low-score group showed estrogen-response-driven signaling. To further enhance predictive accuracy, a genomic model (Lumi-G) based on 22 established RNA biomarkers was further developed and integrated with the Lumi-CI model to create a multimodal Lumi-CIG model. We subsequently designed a Lumi-Guide 2-step triage system that prioritizes clinical-imaging information (Lumi-CI) while selectively incorporating genomic data (Lumi-CIG) when beneficial, thus optimizing resource allocation. Critically, by integrating Lumi-Guide system prestratification with response-predictive-subtype-recommended therapies, patients predicted to achieve pCR demonstrated substantially higher actual pCR rates than controls across 3 treatment patterns: 77.8% vs. 61.5% (optimal treatment), 57.6% vs. 24.7% (non-optimal treatment), and 35.3% vs. 4.7% (double-negative response-predictive subtype). This clinically practical and biologically interpretable framework transforms personalized neoadjuvant therapy in luminal breast cancer into a scalable reality.