Background and Objectives: Breast cancer (BC) is a global health concern for women; the disease contributes to significant morbidity and mortality. A key element in the diagnosis of BC involves the histopathological diagnosis, which determines patient management and therapy. However, BC is a multifaceted disease, limiting access to early diagnosis and, therefore, treatment. Artificial intelligence (AI) is transforming diagnostics in the medical field, especially in the detection of BC. Due to the increased availability of digital slides, it has facilitated the effective integration of AI in breast cancer diagnosis. Diagnosis poses a great challenge, even for experienced pathologists, due to the heterogeneity of this malignancy. Analysing microscopic slides by pathologists requires a considerable amount of time. Implementation of AI into routine workflows holds potential to improve diagnostic sensitivity and inter-observer concordance, and to increase efficiency by reducing the review time, thereby helping to alleviate the burden of diagnosing BC. Previous studies mainly address imaging modalities or oncology broadly, while a few specifically concentrates on the histopathological aspect of breast cancer. This review aims to explore the novel synthesis of AI advancements in digital pathology, including tumour classification, grading, lymph node staging, and biomarker evaluation, and discuss their potential incorporation into clinical workflows. We will also discuss the current barriers and prospects for future advancements. Materials and Methods: A literature search was conducted in PubMed and Google Scholar using the mentioned keywords. Articles published in English until July 2025 were reviewed and synthesised narratively. Results: Recent studies demonstrate that AI models such as convolutional neural networks (CNNs), YOLO, and RetinaNet achieve high accuracy in tumour detection, histological grading, lymph node metastasis localisation, and biomarker analysis. The reported performance values range from 75% to over 95% accuracy across various tasks, with gains in diagnostic sensitivity and inter-observer concordance, and reduced review time in assisted workflows. However, certain limitations, such as data variability, external validation in clinical practice, and ethical concerns, restrict the growth and optimal performance of AI and its clinical applicability. Conclusions: The future for AI looks promising, as it is rapidly evolving. By analysing evidence across multiple domains, this review evaluates both opportunities and persisting barriers, offering practical overviews for future clinical transition. AI cannot replace pathologists; however, it has the capabilities to enhance diagnostic precision, efficiency, and ultimately patient outcomes. It is only a matter of time before AI is adopted into healthcare.

Background/Objectives: Sentinel lymph node biopsy is central to axillary staging in breast cancer, but conventional mapping often relies on radioisotopes and/or blue dye. Indocyanine green fluorescence has emerged as an alternative, although evidence for its use as a sole tracer in routine practice remains limited. This study evaluated the technical feasibility, lymph node yield, nodal metastasis detection, and short-term clinical outcomes of indocyanine green used as the only tracer for axillary staging in a consecutive single-centre cohort. Methods: This retrospective observational cohort study included 260 patients with histologically confirmed breast cancer who underwent axillary surgery at University Hospital Kaspela between 2024 and 2025 under an institutional protocol using indocyanine green as the sole tracer. Indocyanine green-guided mapping was attempted in all patients. For node-focused statistical analyses, a predefined complete-case–cohort of 230 patients was used. Descriptive analyses assessed axillary procedure distribution, lymph node yield, nodal metastasis, and postoperative outcomes. Exploratory multivariable logistic regression was performed to evaluate predictors of nodal metastasis. Results: Mapping was successful in 259/260 patients (99.6%). In the complete-case–cohort, sentinel lymph node biopsy was performed in 166/230 patients (72.2%), targeted axillary dissection in 4/230 (1.7%), and axillary lymph node dissection in 60/230 (26.1%). Median overall lymph node yield was 4 (IQR 3–7), but this pooled value reflected heterogeneous axillary procedures and should not be interpreted as sentinel node yield alone. In the clinically node-negative upfront SLNB subgroup, median lymph node yield was 4 (IQR 2.75–5), and nodal metastasis was identified in 22/112 patients (19.6%). Overall, nodal metastasis was identified in 58/230 patients (25.2%), with a median of 2 metastatic nodes (IQR 1–3) among nodal-positive cases. Reoperation for axillary lymph node dissection occurred in 14/230 patients (6.1%). In exploratory multivariable analysis, suspicious biopsied-positive nodes (OR 12.85, 95% CI 3.98–41.52), suspicious non-biopsied nodes (OR 15.58, 95% CI 3.44–70.59), and neoadjuvant therapy (OR 0.31, 95% CI 0.11–0.87) were associated with nodal metastasis; these findings should be interpreted cautiously given the expected clinical relationship between preoperative nodal suspicion and nodal positivity, and the limited number of nodal-positive events. Conclusions: Indocyanine green used as a sole tracer demonstrated high technical feasibility within a heterogeneous real-world axillary staging workflow in this single-centre cohort. These findings should be interpreted as implementation-focused feasibility data rather than formal diagnostic validation, given the retrospective design, heterogeneous case mix, and absence of an internal comparator.