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: Breast cancer is the most prevalent neoplasm in women. Improved screening and systemic therapies have allowed more patients to choose breast-conserving surgery over mastectomy. However, preserving glandular tissue while achieving negative margins remains difficult. Traditional intraoperative margin assessment techniques like frozen section analysis, cavity shave margins, intraoperative ultrasonography, and specimen radiography aim to reduce positive margins and re-excision rates but face several limitations, including time consumption, interpretive challenges, and operator dependency. Our aim was to critically evaluate both conventional and emerging intraoperative margin assessment techniques in breast-conserving surgery, highlighting their clinical utility, limitations, and potential to reduce re-excision rates and improve patient outcomes. Methods: We assessed PubMed and Google Scholar databases using search terms such as specimen radiography, intraoperative ultrasonography, mass spectrometry, optical coherence tomography, artificial intelligence, and others. Studies were selected based on relevance, language, and completeness, and refined through author consensus. Conclusions: Conventional techniques have demonstrated value in reducing re-excisions and preserving cosmetic outcomes. Emerging tools like MarginProbe, fluorescence imaging, mass spectrometry (MasSpec Pen, iKnife), OCT, and AI-enhanced imaging show promise in offering real-time feedback and higher diagnostic accuracy. However, high costs, training needs, and data variability limit their widespread adoption. Investment in standardised protocols and multicentre trials is essential. Integration of imaging, spectroscopy, and AI may offer the most robust framework for improving surgical outcomes and quality of life for breast cancer patients.

Introduction: The essential tools doctors need to diagnose breast cancer tumors at the early stages and with no clinical presentation are screening mammography and ultrasonography. Nonpalpable breast lesions are a current problem nowadays and there are various types of navigation techniques that are utilized in order to achieve cancer cell-free resection margins in the first place. Intraoperative ultrasound has been proven to be a safe and effective way to excise this type of breast tumors. Aim: To study the correlation between the experience we obtained while using intraoperative ultrasound for excision of nonpalpable breast lesions, and the duration of surgery. Materials and methods: Twenty-five women with nonpalpable breast lesions underwent breast-conserving surgery using ultrasound navigation. Nominal and ordinal data were presented using numbers and percentages. Spearman’s rank-order correlation was used to assess the effect of experience gained on the duration of surgery. Results: The tumor distribution was 72% malignant tumors and 28% benign. A 100% identification rate in the effective surgical excisions was reported. In the process of gaining experience using the method, the duration of surgery shortened by 30%. No tumor cells on ink were found in all specimens and there was no need for secondary surgeries. Conclusions: Our results demonstrate a relatively short learning curve, and we believe that breast surgeons should be helped and encouraged to gather adequate expertise to ensure patient safety and gain confidence.

Introduction: Gastrointestinal stromal tumors (GISTs) usually present as an exophytic mass localized in stomach. Years ago, conventional surgery was the only option. Laparoscopic and robotic procedures have risen to prominence in recent decades as technology and surgical techniques have advanced.Aim: The aim of this study was to evaluate the safety and feasibility of robotic surgery of GIST.Materials and methods: All patients who underwent robotic surgery for a GIST in the University Hospital Kaspela-Plovdiv between November 2021 and April 2022 were included. Post-operative course and short oncological outcomes were analyzed.Results: Four patients with gastric GIST, median size 50 mm [40–70 mm], underwent robotic surgery. Conversion to open surgery not occurred. We have two cases with localization in corpus, one in antrum and one in the fundus. We have registered one complicationsanastomotic leakage after proximal resection treated conservatively. In others, the post-operative period was uneventful. The average hospital stay was 8.2 (4-11) days. The median operating time was 255 min (220–320 min).Conclusions: On basis of this initial results, we can conclude that robotic gastrointestinal tumors surgery is safe, feasible, and oncological sustained. Of course, one of the weakest sides of this surgery is prolonged operative time and increased expenses.

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.