Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Skin Cancer

Skin Cancer

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...

You might also read

Related Articles

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

Sort by

Same author

Moral MedTech: Exploring Ethics-by-Design for Artificial Intelligence in Healthcare via Serious Gaming.

Studies in health technology and informatics·2026

Same author

Explainable Machine Learning for Parkinson's Disease Screening Using Spiral Drawing Test.

Studies in health technology and informatics·2026

Same author

Healthy living environments create healthy physical activity-related habits (ELEVATE): A protocol for a smartphone-based mixed-method study.

PloS one·2026

Same author

Designing effective explainable AI: a human-centered evaluation of explanation formats in financial decision-making.

Frontiers in artificial intelligence·2026

Same author

Understanding the Role of Clinical Decision Support Systems Among Hospital Nurses Using the FITT (Fit Between Individuals, Tasks, and Technology) Framework: Qualitative Study.

Journal of medical Internet research·2025

Same author

Enhancing Clinical Decision Support: A Heuristic Evaluation of Explainable AI in Healthcare Dashboards.

Studies in health technology and informatics·2025

Search research articles

Related Experiment Video

Updated: May 1, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Exploring Differential Diagnosis-Based Explainable AI: A Case Study in Melanoma Detection.

Bjorn Buijing¹, Danielle Sent^1,2

¹HU University of Applied Sciences Utrecht, Research Group Artificial Intelligence, Utrecht, The Netherlands.

Studies in Health Technology and Informatics

|May 17, 2025

Summary

This summary is machine-generated.

A new Explainable Artificial Intelligence (AI) method improves trust and usability for AI-driven melanoma detection. This novel approach is more effective than existing methods, including saliency mapping.

Keywords:

Explainable AI Melanoma detection differential diagnosis human-centred evaluation imaging

More Related Videos

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Published on: March 8, 2022

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Related Experiment Videos

Last Updated: May 1, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Published on: March 8, 2022

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Area of Science:

Dermatology
Medical Artificial Intelligence
Computer Science

Background:

Melanoma poses a significant global health risk, with increasing incidence and mortality rates, particularly when diagnosed late.
Deep learning Artificial Intelligence (AI) models show promise in melanoma detection but often lack transparency, hindering clinical trust.
The complexity of AI models makes it challenging for clinicians to understand and rely on AI-generated diagnoses.

Purpose of the Study:

To introduce a novel Explainable AI (XAI) method for melanoma detection.
To enhance the transparency and clinical utility of AI diagnostic tools.
To provide explanations that align with clinical differential diagnosis practices.

Main Methods:

Development of a novel XAI method integrated with differential diagnosis principles.
Evaluation of the novel XAI method against four common XAI techniques, including saliency mapping.
User study assessing perceived usability and trust among clinicians with the tested XAI methods.

Main Results:

The novel XAI method was perceived as more useful by intended users compared to existing techniques.
Saliency mapping, a widely used XAI technique, received the lowest usability and trust ratings.
The novel method demonstrated superior performance in enhancing clinician understanding and trust in AI-based melanoma detection.

Conclusions:

The developed XAI method offers a more transparent and clinically relevant approach to AI in melanoma diagnosis.
Current widely used XAI methods like saliency mapping may not be suitable for clinical deployment in dermatology.
Explainable AI techniques that align with clinical reasoning are crucial for fostering trust and adoption of AI in healthcare.