AIMC Topic: Dermatologists
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Dermatologist-level classification of skin cancer with deep neural networks.
Nature
Jan 25, 2017
Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of ski...
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study.
Nature communications
May 21, 2025
Artificial intelligence (AI) systems substantially improve dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing their confidence and trust in AI-driven decisions. Despite these advancements, there rema...
Mobile health apps for skin cancer triage in the general population: a qualitative study on healthcare providers' perspectives.
BMC cancer
May 9, 2025
BACKGROUND: Mobile health (mHealth) applications (apps) integrated with artificial intelligence for skin cancer triage are increasingly available to the general public. Nevertheless, their actual uptake is limited. Although endorsement by healthcare ...
Physician Opinions on Artificial Intelligence Chatbots In Dermatology: A National Online Cross-Sectional Survey of Dermatologists.
Journal of drugs in dermatology : JDD
Nov 1, 2024
BACKGROUND: Artificial intelligence chatbots (AIC) have sharply risen in popularity. Dermatology, heavily involving visual, clinical, and pathological pattern-recognition techniques, will be impacted by AIC. Thus, this study aims to categorize the at...
ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology.
Clinical and experimental dermatology
Jun 25, 2024
BACKGROUND: ChatGPT is an online language-based platform designed to answer questions in a human-like way, using deep learning -technology.
Unveiling the power of convolutional neural networks in melanoma diagnosis.
European journal of dermatology : EJD
Oct 1, 2023
Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, ...
Acceptability of artificial intelligence among Indian dermatologists.
Indian journal of dermatology, venereology and leprology
Jan 1, 2022
The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.
American journal of clinical dermatology
Mar 1, 2021
Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. Howev...
Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images.
Science translational medicine
Feb 17, 2021
A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to improved melanoma prognosis an...
Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population.
Chinese medical journal
Sep 5, 2020
BACKGROUND: Diagnoses of Skin diseases are frequently delayed in China due to lack of dermatologists. A deep learning-based diagnosis supporting system can facilitate pre-screening patients to prioritize dermatologists' efforts. We aimed to evaluate ...
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