Cutaneous malignancies are common malignancies worldwide, with rising incidence. Most skin cancers, including melanoma, can be cured if diagnosed correctly at an early stage. Thus, millions of biopsies are performed annually, posing a major economic burden. Non-invasive skin imaging techniques can aid in early diagnosis and save unnecessary benign biopsies. In this review article, we will discuss in vivo and ex vivo confocal microscopy (CM) techniques that are currently being utilized in dermatology clinics for skin cancer diagnosis. We will discuss their current applications and clinical impact. Additionally, we will provide a comprehensive review of the advances in the field of CM, including multi-modal approaches, the integration of fluorescent targeted dyes, and the role of artificial intelligence for improved diagnosis and management.

Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.

Regenerative medicine holds significant promise for addressing diseases and irreversible damage that are challenging to treat with conventional methods, making it a prominent research focus in modern medicine. Research on stem cells, a key area within regenerative medicine due to their self-renewal capabilities, is expanding, positioning them as a novel therapeutic option. Stem cells, utilized in various treatments, are categorized based on their differentiation potential and the source tissue. The term ‘stem cell’ encompasses a broad spectrum of cells, which can be derived from embryonic tissues, adult tissues, or generated by reprogramming differentiated cells. These cells, applied across numerous medical disciplines including cardiovascular, neurological, and hematological disorders, as well as wound healing, demonstrate varying therapeutic applications based on their differentiation capacities, each presenting unique advantages and limitations. Nevertheless, the existing literature lacks a comprehensive synthesis examining stem cell therapy and its cellular subtypes across different medical specialties. This review addresses this lacuna by collectively categorizing contemporary stem cell research according to medical specialty and stem cell classification, offering an exhaustive analysis of their respective benefits and constraints, thereby elucidating multifaceted perspectives on the clinical implementation of this therapeutic modality.

Despite therapeutic advancements, malignant melanoma remains a leading cause of skin cancer-related mortality, with incidence continuing to rise globally. Traditional prognostic tools offer important clinical guidance but fail to capture the biological heterogeneity of melanoma or reliably predict responses to emerging therapies. In this review, we summarize recent advances in prognostic and predictive molecular biomarkers reported over the past five years. We discuss immunohistochemical and tissue-based markers, circulating biomarkers, microRNAs, and gene expression profiles that enhance risk stratification and inform surveillance strategies. We also review immune-related markers that may predict response to immune-checkpoint inhibitor therapy. Lastly, we highlight investigational biomarkers—including gene signatures, epigenomic alterations, and microbiome influences—that are shaping the future landscape. Together, these advances reflect a shift toward precision oncology in melanoma, with the integration of biomarker-driven strategies offering the potential to personalize treatment and improve patient outcomes.

Introduction: The COVID-19 pandemic drastically changed the priorities in healthcare services; outpatient management of acne has changed during this period. Objectives: We aimed to investigate treatment practices, outcomes and identify modified follow-up schedules applied during the pandemic. Methods: The patients who were admitted to dermatology outpatient clinic between March 13 and July 13, 2020, were included. Patients who were admitted between March 13 and July 13, 2019, were served as controls for the study. For each patient, age, gender, treatment protocols, treatment intervals, compliance with the treatment, treatment modifications, and adverse events were recorded. Results: The total number of acne patients admitted to dermatology outpatient clinics during the pandemic period was 278 and consisted of 12.3% (278) of all admissions. Isotretinoin treatment was started in only 16 (5.8%) of the patients. The proportion of patients who were under follow-up was significantly higher during the pandemic period (P < 0.005). There was no difference between the pandemic period and the non-pandemic period in terms of starting isotretinoin treatment (P > 0.05). During pandemic period, 79% of the patients who used isotretinoin were followed-up every two or more months. Extended follow-up intervals showed no difference for detecting side effects (P > 0.05). Conclusions: Acne patients constitute an important part of dermatology outpatient clinics. During the pandemic period, the majority of acne patients came for follow-up. Extended follow-up periods were adopted by physicians and were found safe and effective in the current study. Thus, isotretinoin treatment seems efficacious and safe during pandemic period.

Mycosis fungoides (MF) is the most prevalent type of cutaneous T cell lymphomas. Studies on the prognosis of MF are limited, and no research exists on the potential of artificial intelligence to predict MF prognosis. This study aimed to compare the predictive capabilities of various machine learning (ML) algorithms in predicting progression, treatment response, and relapse and to assess their predictive power against that of the Cox proportional hazards (CPH) model in patients with early-stage MF. The data of patients aged 18 years and over who were diagnosed with early-stage MF at Ankara University Faculty of Medicine Hospital from 2006 to 2024 were retrospectively reviewed. ML algorithms were utilized to predict complete response, relapse, and disease progression using patient data. Of the 185 patients, 94 (50.8%) were female, and 91 (49.2%) were male. Complete response was observed in 114 patients (61.6%), while relapse and progression occurred in 69 (37.3%) and 54 (29.2%) patients, respectively. For predicting progression, the Support Vector Machine (SVM) algorithm demonstrated the highest success rate, with an accuracy of 75%, outperforming the CPH model (C-index: 0.652 for SVM vs. 0.501 for CPH). The most successful model for predicting complete response was the Ensemble model, with an accuracy of 68.89%, surpassing the CPH model (C-index: 0.662 for the Ensemble model vs. 0.543 for CPH). For predicting relapse, the decision tree classifier showed the highest performance, with an accuracy of 78.17%, outperforming the CPH model (C-index: 0.782 for the decision tree classifier vs. 0.505 for CPH). The results suggest that ML algorithms may be useful in predicting prognosis in early-stage MF patients.