Veselin R. Chonov

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.

Introduction: Onychomycosis is a frequent nail disorder, accounting for up to 50% of all nail problems. Treatment of onychomycosis is expensive and requires a long time of antifungal medications. Consequently, a proper and faster diagnosis is necessary. Especially for those patients with diabetes mellitus, where onychomycosis is among the most significant predictors of foot ulcer and possible severe complications.Aim: To compare the sensitivity, specificity, and turnaround time between direct microscopy, culture, histology, and real-time PCR. In addition, to compare the frequency and etiology of onychomycosis in patients with and without DM.Materials and methods: This study included 102 patients, divided into two groups. One group consisted of patients with diabetes mellitus and the other – without diabetes. Nail samples were collected and examined by direct KOH microscopic examination, culture, histology, and real-time PCR.Results: From the 102 patients with clinical onychomycosis, positive KOH was found in 38 (37.3%). Culture – 82 out of 102 samples (80.4%) were positive for dermatophytes, yeasts, and/or NDM. Positive histology samples were 32 (41.6%). The PCR was positive in 57 (55.9%) out of the 102. We discovered that there is no significant statistical difference in the etiology of the fungal infections between the two groups.Conclusions: All mycological investigations have their place in the diagnosis of onychomycosis. Direct microscopy, culture, and histology are useful methods for clinicians to diagnose and follow up the post-treatment period. The advantages of RT-PCR include obtaining results faster and accurately identifying fungi, thus becoming more valued in the diagnosis of OM.