Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

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Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review

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Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

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Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

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Characteristics of publicly available skin cancer image datasets: a systematic review

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Artificial Intelligence in Dermatology: Challenges and Perspectives

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Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4

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A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

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Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices

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Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems: A Systematic Review

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AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions

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Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification

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Deep learning in dermatology: a systematic review of current approaches, outcomes, and limitations

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A deep-learning algorithm to classify skin lesions from mpox virus infection

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Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging

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Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

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Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis

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Explainable Deep Learning Models With Gradient-Weighted Class Activation Mapping for Smart Agriculture

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Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

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Systematic reviews of machine learning in healthcare: a literature review

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Evaluation of Artificial Intelligence–Assisted Diagnosis of Skin Neoplasms: A Single-Center, Paralleled, Unmasked, Randomized Controlled Trial

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Performance of a deep neural network in teledermatology: a single‐centre prospective diagnostic study

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Robust Skin Disease Classification by Distilling Deep Neural Network Ensemble for the Mobile Diagnosis of Herpes Zoster

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Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

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A benchmark for neural network robustness in skin cancer classification

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Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics

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Artificial Intelligence: Review of Current and Future Applications in Medicine

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Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach

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Artificial intelligence and skin cancer

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Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm

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Robustness of convolutional neural networks in recognition of pigmented skin lesions

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Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study

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Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review

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Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians

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Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

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Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

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Predicting the clinical management of skin lesions using deep learning

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Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis

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AI in dermatology: a comprehensive review into skin cancer detection

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Statistical Tests for Replacing Human Decision Makers with Algorithms

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Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network

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Explainability agreement between dermatologists and five visual explanations techniques in deep neural networks for melanoma AI classification

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Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners

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Toward automated severe pharyngitis detection with smartphone camera using deep learning networks

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Dermatologists’ Perspectives on Artificial Intelligence and Augmented Intelligence — A Cross-sectional Survey

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Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects

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Modern artificial intelligence and large language models in graduate medical education: a scoping review of attitudes, applications & practice

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Skin Type Diversity in Skin Lesion Datasets: A Review

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Store-and-Forward Images in Teledermatology: Narrative Literature Review

Jiang, S. W., Flynn, M. S., Kwock, J. T., & Nicholas, M. W. (2022) · JMIR Dermatology · doi:10.2196/37517 · Cited by 32

Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods

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Diagnostic clinical decision support based on deep learning and knowledge-based systems for psoriasis: From diagnosis to treatment options

Yaseliani, M., Maghsoodi, A. I., Hassannayebi, E., & Aickelin, U. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109754 · Cited by 34

Convolutional neural network assistance significantly improves dermatologists’ diagnosis of cutaneous tumours using clinical images

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Artificial intelligence for the automated single‐shot assessment of psoriasis severity

Okamoto, T., Kawai, M., Ogawa, Y., Shimada, S., & Kawamura, T. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18354 · Cited by 32

Exploring the Effectiveness of Artificial Intelligence, Machine Learning and Deep Learning in Trauma Triage: A Systematic Review and Meta-Analysis

Adebayo, O., Bhuiyan, Z. A., & Ahmed, Z. (2022) · Digital Health · doi:10.1177/20552076231205736 · Cited by 39

Accuracy and clinical relevance of an automated, algorithm‐based analysis of facial signs from selfie images of women in the United States of various ages, ancestries and phototypes: A cross‐sectional observational study

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The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review

Escalé-Besa, A., Vidal-Alaball, J., Miró Catalina, Q., Gracia, V. H. G., Marin-Gomez, F. X., & Fuster-Casanovas, A. (2024) · Healthcare · doi:10.3390/healthcare12121192 · Cited by 29

AI in Aesthetic/Cosmetic Dermatology: Current and Future

Thunga, S., Khan, M., Cho, S. I., Na, J., & Yoo, J. Y. (2024) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.16640 · Cited by 30

Artificial Intelligence in Cervical Cancer Screening: Opportunities and Challenges

Dellino, M., Cerbone, M., d’Amati, A., Bochicchio, M. A., Laganà, A. S., Etrusco, A., Malvası, A., Vitagliano, A., Pinto, V., Cicinelli, E., et al. (2024) · AI · doi:10.3390/ai5040144 · Cited by 37

Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement

Navarrete‐Dechent, C., Liopyris, K., & Marchetti, M. A. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.06.040 · Cited by 29

Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives

Zbrzezny, A. M., & Krzywicki, T. (2025) · Applied Sciences · doi:10.3390/app15147856 · Cited by 37

Legal and ethical considerations of artificial intelligence in skin cancer diagnosis

Jobson, D., Mar, V., & Freckelton, I. R. (2021) · Australasian Journal of Dermatology · doi:10.1111/ajd.13690 · Cited by 31

Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification

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Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review

Nazari, S., & García, R. (2023) · Life · doi:10.3390/life13112123 · Cited by 31

AI ‐generated dermatologic images show deficient skin tone diversity and poor diagnostic accuracy: An experimental study

Joerg, L., Kabakova, M., Wang, J. Y., Austin, E., Cohen, M., Kurtti, A., & Jagdeo, J. R. (2025) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.20849 · Cited by 28

A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

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Classification of skin lesions with generative adversarial networks and improved MobileNetV2

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The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks

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DermX: An end-to-end framework for explainable automated dermatological diagnosis

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AN EFFICIENT SKIN CANCER PROGNOSIS STRATEGY USING DEEP LEARNING TECHNIQUES

Dr., S. R. S., Dr., C. S. K., & Latha, A. G. (2021) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2021/v12i1/211201180 · Cited by 23

Automated Deep Learning based Disease Prediction Using Skin Health Records: Issues, Challenges and Future Directions

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A comprehensive review on step-based skin cancer detection using machine learning and deep learning methods

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Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients

Lerma, M., & Lucas, M. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/awjv6348 · Cited by 26

Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists

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A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges

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Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4Preprint

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Augmented Intelligence: Enhancing Human Decision Making

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Improved Image Analysis for Measuring Gastric Ulcer Index in Animal Models and Clinical Diagnostic Data

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Algorithmic transparency and interpretability measures improve radiologists’ performance in BI-RADS 4 classification

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Smart Low Level Laser Therapy System for Automatic Facial Dermatological Disorder Diagnosis

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Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit

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Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet

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Objective and automatic grading system of facial signs from selfie pictures of South African women: Characterization of changes with age and sun‐exposures

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Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: A systematic review

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Bridging the Digital Divide: A Practical Roadmap for Deploying Medical Artificial Intelligence Technologies in Low-Resource Settings

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A deep convolutional neural network for Kawasaki disease diagnosis

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Artificial Intelligence in Cutaneous Oncology

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Dissection of medical AI reasoning processes via physician and generative-AI collaborationPreprint

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Artificial Intelligence in Dermatology: A Systematized Review

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The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search

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Classification of clinical skin lesions with double-branch networks

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Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies

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Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present

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Decoding the NCCN Guidelines With AI: A Comparative Evaluation of ChatGPT-4.0 and Llama 2 in the Management of Thyroid Carcinoma

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Accuracy of Deep Neural Network in Triaging Common Skin Diseases of Primary Care Attention

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Augmenting the accuracy of trainee doctors in diagnosing skin lesions suspected of skin neoplasms in a real-world setting: A prospective controlled before-and-after study

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Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population

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Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks AlgorithmsPreprint

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Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017

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