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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...

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Prospective Evidence on Artificial Intelligence-Assisted Melanoma Diagnostics: A Systematic Review and Meta-Analysis.

Sara Laiouar-Pedari¹, Arlene Kühn¹, Christoph Wies^1,2

¹Division of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center, Heidelberg, Germany.

JAMA Dermatology

|March 25, 2026

Summary

Artificial intelligence (AI) systems show comparable diagnostic performance to dermatologists in melanoma detection. AI may improve diagnostic accuracy when used as a decision-support tool, but further validation is needed.

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Area of Science:

Dermatology and Medical Imaging
Artificial Intelligence in Healthcare
Diagnostic Accuracy Studies

Background:

Dermoscopy is a standard for melanoma diagnosis.
Artificial intelligence (AI) is being explored as a decision-support tool for melanoma detection.
Prospective evidence is crucial to compare AI performance against dermatologists.

Purpose of the Study:

To evaluate the diagnostic performance of dermatologists, AI systems, and AI-assisted dermatologists in prospective melanoma detection studies.
To assess the clinical readiness of AI for melanoma diagnostics.

Main Methods:

Systematic review and meta-analysis of prospective studies using dermoscopic images.
Inclusion of studies with histopathologic confirmation and sufficient melanoma cases.
Data extraction and synthesis of sensitivity, specificity, accuracy, and balanced accuracy.

Main Results:

Eleven prospective studies (2500+ patients, 50 dermatologists) were analyzed.
Dermatologists: sensitivity 78.6%, specificity 75.2%. AI alone: sensitivity 80.9%, specificity 75.6%.
AI-assisted dermatologists showed higher sensitivity (91.9%) and specificity (83.7%); AI demonstrated higher specificity in direct comparisons.

Conclusions:

AI systems perform comparably to dermatologists in melanoma diagnostics.
AI shows potential to enhance diagnostic performance as a decision-support tool.
High risk of bias and limited generalizability necessitate broader validation in unselected populations.