Open research questions in Cutaneous Melanoma Detection and Management

What the literature leaves open

• Existing methods often struggle to capture complex lesion characteristics. Traditional diagnostic methods are subjective, invasive, and unsuitable for large-scale screening. There is a need for a non-invasive, high-precision alternative for analyzing skin lesions.

  Enhancing Early Skin Cancer Detection: A Deep Learning Approach with Multi-Scale Feature Refinement and Fusion · 2026 · DOI

• Investigating the application of Fed-GPL to other medical imaging tasks, - Evaluating the performance of Fed-GPL on larger-scale datasets, - Exploring the use of Fed-GPL in other domains beyond medical image analysis

  Federated generative prompt learning with vision foundation models: universal efficient multi-center medical image analysis · 2026 · DOI

• The lack of a universal and efficient federated prompt learning framework for multi-center medical image analysis. The challenge of mitigating feature shifts in multi-center medical data. The need to reduce communication overhead and improve generalization performance in federated learning.

  Federated generative prompt learning with vision foundation models: universal efficient multi-center medical image analysis · 2026 · DOI

• The challenge of distinguishing between benign and malignant spitzoid tumours due to overlapping features. The need for high-quality dermoscopic images and accurate histopathological confirmation. The challenge of achieving high positive predictive value in dermoscopic image analysis.

  Diagnostic Accuracy of Dermoscopy for Spitzoid Tumours in Adults · 2026 · DOI

• The lack of clear understanding of dermoscopy's accuracy in detecting spitzoid tumours. The need for improved diagnostic techniques to distinguish between benign and malignant spitzoid tumours. The gap in current research on the diagnostic accuracy of dermoscopy for spitzoid tumours in adults.

  Diagnostic Accuracy of Dermoscopy for Spitzoid Tumours in Adults · 2026 · DOI

• Lymph node (LN) metastasis predicts poor patient outcomes, but the mechanistic drivers that shape metastatic fitness, immune evasion, and clinical impact remain elusive.

  Melanoma evolution in the lymph node shapes systemic outcomes · 2026 · DOI

• However, validated risk prediction tools for this specific population are lacking.

  Development and validation of a risk assessment model for post-surgical scar formation in pediatric melanocytic nevus excision: a retrospective cohort study · 2026 · DOI

• Additional investigation into whether these differences are found in other specialized fields could inform future postgraduate training and research.

  Exploratory survey study of differences in knowledge, attitudes, and practices of outpatient older adult clinical care between dermatologists, primary care physicians and geriatricians across three academic medical centers · 2026 · DOI

• Finally, we identify critical gaps in understanding the mechanisms of resistance, CNS progression, and the immunogenic landscape of KIT-driven melanoma.

  KIT-Mutant Melanoma: Understanding the Pathway to Personalized Therapy · 2025 · DOI

• While the current literature shows promising results in the evaluation of non-melanoma skin cancer, only limited research exists on the application of EVCM in melanocytic lesions.

  Benign or Malignant? Ex Vivo Confocal Laser Scanning Microscopy for Bedside Histological Assessment of Melanocytic Lesions · 2025 · DOI

• CONCLUSIONS: We found sex differences and several new possible etiological factors behind benign skin tumors which, despite being common, remain poorly characterized.

  Benign skin tumors in older persons: a population-based study · 2025 · DOI

• However, its applications to sexually transmitted infections (STIs) remain unclear.

  Use of AI in Identification of Sexually Transmitted Infections and Anogenital Dermatoses · 2025 · DOI

• Despite recent successes with combination immune checkpoint inhibitors in the treatment of affected patients, the mechanistic underpinnings of T-cell entry and response to these drugs in brain metastasis are poorly understood.

  Peritumoral Venous Vessels: Autobahn and Portal for T Cells to Melanoma Brain Metastasis · 2024 · DOI

• While Ensemble Learning has become an integral component of both Machine Learning (ML) and Deep Learning (DL) methodologies, a specific technique ensuring optimal allocation of weights for each model's prediction is currently lacking.

  A Multi-level ensemble approach for skin lesion classification using Customized Transfer Learning with Triple Attention · 2024 · DOI

• Despite several prospective randomised trials, the optimal extent of excision margin remains controversial, and this is reflected in the persistent lack of consensus in guidelines globally.

  A Review of Contemporary Guidelines and Evidence for Wide Local Excision in Primary Cutaneous Melanoma Management · 2024 · DOI

• Restrictions to health care specialists, staff shortages, and fear of SARS-CoV-2 infection led to interruptions in routine care, such as early melanoma detection; however, premature mortality and economic burden associated with this postponement have not been studied yet.

  Health Economic Consequences Associated With COVID-19–Related Delay in Melanoma Diagnosis in Europe · 2024 · DOI

• The study used data from a large-scale survey, but the analysis was limited to participants with complete data. The study did not account for all potential confounding variables.

  Machine learning-based association analysis of triglyceride-glucose index with melanoma prevalence and all-cause mortality: insights from cross-sectional NHANES 1999–2018 data and an external hospital-based dataset · 2026 · DOI

• Biology-driven, novel treatment approaches tailored to primary cervical melanoma are needed. Further research is required to understand the aggressive biology of primary cervical melanoma. The use of adjuvant pelvic radiotherapy in primary cervical melanoma should be investigated.

  Primary malignant melanoma of the uterine cervix: a case report of aggressive progression despite multimodal therapy · 2026 · DOI

• There is a lack of robust evidence specific to the cervical origin of primary malignant melanoma. Current treatment strategies are largely extrapolated from experience with cutaneous melanoma and other mucosal sites.

  Primary malignant melanoma of the uterine cervix: a case report of aggressive progression despite multimodal therapy · 2026 · DOI

• Further investigation of HSI as a complementary imaging modality for early melanoma detection. Development of computer-aided diagnostic systems for melanoma detection using HSI.

  Minimizing the False Negative Rate in Convolutional Neural Network-Based Melanoma Classification Using Hyperspectral Data to Reduce Misdiagnosis · 2026 · DOI

• There is a need to reduce the false negative rate in melanoma classification. Current diagnostic methods have limitations, including false positive and false negative errors.

  Minimizing the False Negative Rate in Convolutional Neural Network-Based Melanoma Classification Using Hyperspectral Data to Reduce Misdiagnosis · 2026 · DOI

• intelligence to Fig. No. 3: AI in cosmetology. TREATMENT SIMULATIONS Generative AI creates before and after visualizations for the procedures like laser treatments, Botox, Fillers, skin resurfacing, and stimulating up to 10 skin issues such as pores, redness, or dark circles. Tools like prefect Corp’s simulator or eMI’s AI predictor offer realistic previews from selfies, aiding patient education and decision making and guiding post care, using computer vision and data to analyze skin for acne, wrinkles, pigmentation, and monitoring progress for enhanced precision and patient satisfaction. WORKING Data Analysis: AI algorithms process vast amounts of skin data from images, biosensors, and patient history to find patterns. Computer Vision: Identifies and quantifies skin features like wrinkles and lesions for objective assessment. Machine Learning: Learns from the past data to predict treatment efficacy and personalize plans. BENEFITS Data- Driven Decisions: Dermatologists make more informed, objective choices. Enhanced Precision: Tailored treatments lead to better outcomes. Improved Patient Experience: Better management and continuous support. expectation 2 AI APPLICATIONS INTREATMENT Personalized Skincare & Product Recommendation: AI analyze uploaded images and digital questionnaires to suggest specific products and routines, with brands offering AI-driven platforms for consumers. Predictive Treatment Outcomes: Algorithms predict patient response to lasers, microneedling, or fillers, helping tailor treatments for better safety and results. www.ejbps.com │ Vol 13, Issue 4, 2026. │ ISO 9001:2015 Certified Journal │ 72 Dhruthin et al. European Journal of Biomedical and Pharmaceutical Sciences Advanced skin Analysis: AI- powdered systems offer detailed objective measurements of wrinkles, pores, and texture for precise planning and tracking. Augmented Reality (AR): Apps let users virtually try on treatments to visualize results managing expectations. Image-Based Diagnosis: Computer vision identifies and classifies skin issues from images reducing human error and aiding diagnosis. Robotics: While research – focused, robot- assisted laser treatments aim for greater precision through widespread clinical use is pending. Personalized Post-Care: AI monitors recovery and suggests adjustments like anti-inflammatory creams or antioxidants for healing. 3 PROSED SYSTEM ARCHITECTURE The proposed AI-driven personalized skincare system is designed to analyze facial images, classify skin types, and recommend suitable skincare products.

  A REVIEW ON: ARTIFICIAL INTELLIGENCE IN COSMETIC DERMATOLOGY; ADVANCES IN SKIN ANALYSIS AND PERSONALIZED TREATMENT · 2026 · DOI

• The scarcity of available clinical samples, cancer lines, and subtype-specific mouse models. The limited responsiveness of MM to chemotherapy. The need for strategies to enhance the efficacy of chemotherapy in patients with MM.

  Tissue-specific inflammation induces cell state plasticity with oncogenic addiction in mucosal melanoma · 2026 · DOI

• Clinical validation of the synergistic therapeutic approach in the management of patients with MM. Further investigation into the role of mucosal inflammation in driving melanoma stemness and chemoresistance.

  Tissue-specific inflammation induces cell state plasticity with oncogenic addiction in mucosal melanoma · 2026 · DOI

• Emerging role of explainable AI (XAI) - Future research directions for skin disease diagnosis

  A Comprehensive Survey on Skin Disease Diagnosis Using Image Processing and Deep Learning: Trends, Challenges, and Future Directions · 2026 · DOI

Most-cited papers in Cutaneous Melanoma Detection and Management

• Dermatologist-level classification of skin cancer with deep neural networks · Nature · 2017 · 12,434 citations
• Distinct Sets of Genetic Alterations in Melanoma · New England Journal of Medicine · 2005 · 2,329 citations
• Melanoma · The Lancet · 2018 · 1,374 citations
• Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040 · JAMA Dermatology · 2022 · 1,330 citations
• Individualised neoantigen therapy mRNA-4157 (V940) plus pembrolizumab versus pembrolizumab monotherapy in resected melanoma (KEYNOTE-942): a randomised, phase 2b study · The Lancet · 2024 · 729 citations
• Neoadjuvant Nivolumab and Ipilimumab in Resectable Stage III Melanoma · New England Journal of Medicine · 2024 · 457 citations
• Ethnic Differences Among Patients With Cutaneous Melanoma · Archives of Internal Medicine · 2006 · 308 citations
• An Interpretable Skin Cancer Classification Using Optimized Convolutional Neural Network for a Smart Healthcare System · IEEE Access · 2023 · 223 citations
• DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images · Cancers · 2023 · 220 citations
• DeepSkin: A Deep Learning Approach for Skin Cancer Classification · IEEE Access · 2023 · 202 citations

Most recent work

• Neoadjuvant ipilimumab plus nivolumab in melanoma: 5-year survival and biomarker analysis from the phase 2 PRADO-trial · Nature Medicine · 2026
• Intismeran Autogene Plus Pembrolizumab Versus Pembrolizumab Alone in High-Risk Resected Melanoma: 5-Year Update of the Randomized Phase IIb KEYNOTE-942 Study · Journal of Clinical Oncology · 2026
• Prevalence of Familial Melanoma Genes and Cancer Risk Among Genomically Ascertained Individuals · JAMA Dermatology · 2026
• Framing bias in a large language model: Prompt framing influences ChatGPT's accuracy in melanoma classification. A diagnostic accuracy study · Journal of the American Academy of Dermatology · 2026
• Fusion of multi CNN features with ANN for early classification of melanoma using dermoscopy images · Discover Sustainability · 2026
• Melan-Dx: a knowledge-enhanced vision-language framework improves differential diagnosis of melanocytic neoplasm pathology · npj Digital Medicine · 2026
• Prognostic Value of Non-nodal Regional Metastases in Predicting Sentinel Lymph Node Status in Cutaneous Melanoma: Multicenter Analysis of the Sentinel Lymph Node Working Group Database · Annals of Surgical Oncology · 2026
• Accuracy of Index Lymph Node Pathology in Predicting Overall Response to Neoadjuvant Immunotherapy for Clinical Stage III Melanoma: Results From the Prospective NeoACTIVATE Arm C (NCT03554083) Substudy · Annals of Surgical Oncology · 2026
• Development of a novel RNA modification-based risk model to predict prognosis and immunotherapy response in melanoma using machine learning · Journal of Cancer Research and Clinical Oncology · 2026
• Vulvar Melanoma with Vaginal Extension · New England Journal of Medicine · 2026

Related topics in Medicine