Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Skin Cancer01:30

Skin Cancer

5.6K
Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
5.6K
Clinical Applications of Epidermal Stem Cells01:19

Clinical Applications of Epidermal Stem Cells

3.1K
Epidermal stem cells (EpiSCs) are mainly located at the basal layer of the epidermis. These cells repair minor injuries of the skin and replace dead skin cells. However, EpiSCs’ cannot heal severe wounds such as major burns or those from diabetes or hereditary disorders. In such cases, culturing the epidermal stem cells from the patient is possible and has yielded successful treatment options, such as laboratory-grown skin grafts. These grafts are synthesized using a patient’s own...
3.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Image representation for cutaneous drug reactions in darker skin types in undergraduate medical education resources.

Archives of dermatological research·2024
Same author

Validation of the Delphi Consensus Diagnostic Criteria for Necrobiotic Xanthogranuloma.

JAMA dermatology·2024
Same author

Understanding the patient experience of drug reaction with eosinophilia and systemic symptoms: A qualitative study.

Journal of the American Academy of Dermatology·2024
Same author

Drug reaction with eosinophilia and systemic symptoms: Medication adherence and quality of life in survivors.

The journal of allergy and clinical immunology. In practice·2023
Same author

Drug reaction with eosinophilia and systemic symptoms (DRESS): clinical presentation and outcomes in people of color.

The journal of allergy and clinical immunology. In practice·2023
Same author

Drug reaction eosinophilia and systemic symptoms: Clinical phenotypic patterns according to causative drug.

Journal of the American Academy of Dermatology·2023

Related Experiment Video

Updated: Dec 13, 2025

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

433

User satisfaction with a smartphone-compatible, artificial intelligence-based cutaneous pigmented lesion evaluator.

Yen Po Harvey Chin1, I Hsin Huang2, Ze Yu Hou3

  • 1Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan; Department of Biomedical Informatics, Harvard Medical School, MA, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA, USA.

Computer Methods and Programs in Biomedicine
|August 5, 2020
PubMed
Summary

Users reported high satisfaction with MoleMe, an AI-based tool for evaluating skin lesions on smartphones. This suggests AI teledermatology can be widely adopted and benefit patients and physicians.

Keywords:
Artificial intelligenceDeep learningMelanomaPigmented cutaneous lesionTeledermatologyUser satisfaction

More Related Videos

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
02:28

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

674
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

631

Related Experiment Videos

Last Updated: Dec 13, 2025

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

433
Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
02:28

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

674
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

631

Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Teledermatology

Background:

  • Melanoma, an aggressive skin cancer, can develop from pigmented lesions.
  • AI-powered teledermatology offers a new approach to melanoma screening.
  • User satisfaction with smartphone-based AI lesion evaluators requires investigation.

Purpose of the Study:

  • To evaluate user satisfaction with MoleMe, a smartphone-compatible AI tool for assessing cutaneous pigmented lesions.
  • To assess user perceptions regarding interaction, daily life impact, usability, and overall performance of the AI tool.

Main Methods:

  • A user satisfaction questionnaire was administered to participants after using the MoleMe AI evaluator.
  • Data collected from 1231 users in Taiwan between April and May 2019.
  • Statistical analysis (Kruskal-Wallis, Wilcoxon rank-sum tests) compared satisfaction across demographics and risk predictions.

Main Results:

  • Over 90% of users were satisfied, and over 75% were strongly satisfied with MoleMe's usability, interaction, and impact.
  • No significant differences in user satisfaction were found across different age groups, genders, or risk predictions (P > 0.05).

Conclusions:

  • High user satisfaction with AI-based teledermatology tools like MoleMe was observed.
  • These findings indicate potential for widespread adoption and benefit to patients and physicians.
  • Smartphone-based AI lesion evaluation shows promise for melanoma screening.