Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review

Dermatologist-level classification of skin cancer with deep neural networks

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The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

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Scoping reviews: Time for clarity in definition, methods, and reporting

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Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

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Human-computer collaboration for skin cancer recognition

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Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

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A deep learning system for differential diagnosis of skin diseases

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Untitled work

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Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm

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Machine Learning and Health Care Disparities in Dermatology

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Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

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Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

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How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals

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Clinical applications of machine learning algorithms: beyond the black box

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Deep neural networks are superior to dermatologists in melanoma image classification

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Melanoma detection by analysis of clinical images using convolutional neural network

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Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification

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MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images

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Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition

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Superior skin cancer classification by the combination of human and artificial intelligence

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Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the <i>Radiology</i> Editorial Board

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Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

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Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks

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Multimodal skin lesion classification using deep learning

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Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis

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Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

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A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

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A GAN-based image synthesis method for skin lesion classification

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A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions

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Skin typing: Fitzpatrick grading and others

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The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

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Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies

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Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction

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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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Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions

Haenssle, H. A., Fink, C., Toberer, F., Winkler, J. K., Stolz, W., Deinlein, T., Hofmann‐Wellenhof, R., Lallas, A., Emmert, S., Buhl, T., et al. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2019.10.013 · Cited by 237 · see citing works

Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions

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Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

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Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms

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Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network

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Development of a light‐weight deep learning model for cloud applications and remote diagnosis of skin cancers

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Acral melanoma detection using a convolutional neural network for dermoscopy images

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A deep learning, image based approach for automated diagnosis for inflammatory skin diseases

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Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge

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The effects of skin lesion segmentation on the performance of dermatoscopic image classification

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Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels

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Melanoma recognition by a deep learning convolutional neural network—Performance in different melanoma subtypes and localisations

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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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Melanoma detection using adversarial training and deep transfer learning

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Deep learning based classification of facial dermatological disorders

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Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

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A machine learning‐based, decision support, mobile phone application for diagnosis of common dermatological diseases

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Color Medical Image Analysis

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Diagnostic accuracy of content‐based dermatoscopic image retrieval with deep classification features

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Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs

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Data augmentation in dermatology image recognition using machine learning

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Automated detection of erythema migrans and other confounding skin lesions via deep learning

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Scoping review assessing the evidence used to support the adoption of mobile health (mHealth) technologies for the education and training of community health workers (CHWs) in low-income and middle-income countries

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Smart identification of psoriasis by images using convolutional neural networks: a case study in China

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Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas

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Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy

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

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Evaluation of the Number-Needed-to-Biopsy Metric for the Diagnosis of Cutaneous Melanoma

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Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

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Effects of Label Noise on Deep Learning-Based Skin Cancer Classification

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Ros‐NET: A deep convolutional neural network for automatic identification of rosacea lesions

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A superpixel-driven deep learning approach for the analysis of dermatological wounds

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Development and validation of two artificial intelligence models for diagnosing benign, pigmented facial skin lesions

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Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review

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

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Real-time burn depth assessment using artificial networks: a large-scale, multicentre study

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Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions

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Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions

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Atlas of Clinical Dermatology

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Clinically applicable deep learning framework for measurement of the extent of hair loss in patients with alopecia areata

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Clinical photography in skin of colour: tips and best practices

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A Point-of-Care, Real-Time Artificial Intelligence System to Support Clinician Diagnosis of a Wide Range of Skin Diseases

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From Deep Learning Towards Finding Skin Lesion Biomarkers

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Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions

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How to evaluate deep learning for cancer diagnostics – factors and recommendations

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The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images

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Clinically relevant vulnerabilities of deep machine learning systems for skin cancer diagnosis

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Diagnostic performance of the MelaFind device in a real‐life clinical setting

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

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Raising the Bar for Randomized Trials Involving Artificial Intelligence: The SPIRIT-Artificial Intelligence and CONSORT-Artificial Intelligence Guidelines

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A pretrained neural network shows similar diagnostic accuracy to medical students in categorizing dermatoscopic images after comparable training conditions

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Diagnostic capacity of skin tumor artificial intelligence-assisted decision-making software in real-world clinical settings

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Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap

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Reliability and prevalence of digital image skin types in the United States: Results from National Health and Nutrition Examination Survey 2003-2004

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A patient‐oriented, general‐practitioner‐level, deep‐learning‐based cutaneous pigmented lesion risk classifier on a smartphone

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An online platform for interactive feedback in biomedical machine learning

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The vascular morphology of melanoma is related to Breslow index: An in vivo study with dynamic optical coherence tomography

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Performance of a deep learning‐based application for the diagnosis of basal cell carcinoma in Indian patients as compared to dermatologists and nondermatologists

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Bias in medical AI: Implications for clinical decision-making

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Disparities in dermatology AI performance on a diverse, curated clinical image set

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The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool

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Deep learning-aided decision support for diagnosis of skin disease across skin tones

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Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education

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Exploring the risks of automation bias in healthcare artificial intelligence applications: A Bowtie analysis

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Transparent medical image AI via an image–text foundation model grounded in medical literature

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Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

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Systematic Review of Deep Learning Image Analyses for the Diagnosis and Monitoring of Skin Disease

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Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?

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The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective

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Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews

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Review on Federated Learning for digital transformation in healthcare through big data analytics

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Development and preliminary testing of health equity across the AI lifecycle (HEAAL): a framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

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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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Development and clinical evaluation of an artificial intelligence support tool for improving telemedicine photo quality

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Artificial intelligence algorithm bias in information retrieval systems and its implication for library and information science professionals: A scoping review

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The complex ethics of applying ChatGPT and language model artificial intelligence in dermatology

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Using ChatGPT to Improve Health Communication and Plain Language Writing for Students in Communication Sciences and Disorders

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To Triage, To Triage

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Triage nurses’ decisions using the National Triage Scale for Australian emergency departments

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Improving the Prompt Identification of the Emergency Severity Index Level 2 Patient in Triage: Rapid Triage and the Registered Nurse Greeter

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Triage - a South African perspective : main topic

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201: Agreement of a Computerized Triage Tool Using Written Case Scenarios

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