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Related Experiment Video

Updated: Dec 13, 2025

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

Artificial Intelligence in Cutaneous Oncology.

Yu Seong Chu¹, Hong Gi An¹, Byung Ho Oh²

¹Department of Biomedical Engineering, Yonsei University, Wonju, South Korea.

Frontiers in Medicine

|August 6, 2020

Summary

This summary is machine-generated.

Artificial intelligence (AI) aids in diagnosing skin cancer by analyzing smartphone images, complementing dermatologists. This technology, combined with dermoscopy, promises advancements in cutaneous oncology despite current accuracy challenges.

Keywords:

artificial intellegence cutaneous oncology deep learning machine learning skin cancer

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Last Updated: Dec 13, 2025

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

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Published on: August 18, 2022

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

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

Published on: July 11, 2025

Area of Science:

Cutaneous oncology
Medical artificial intelligence
Dermatology

Background:

Skin cancer incidence is rising globally, including in Asian countries.
Skin cancer is visually detectable, unlike many other carcinomas.
Dermatologist expertise is crucial for skin cancer diagnosis and biopsy decisions.

Purpose of the Study:

To review the clinical applications of artificial intelligence (AI) in cutaneous oncology.
To discuss the implementation of AI in diagnosing and managing skin cancer.
To explore AI's potential to augment dermatologist capabilities.

Main Methods:

Comprehensive literature review on AI in dermatology and oncology.
Analysis of AI technologies for analyzing smartphone-captured skin images.
Integration of AI with dermoscopy for enhanced skin lesion inspection.

Main Results:

AI offers a valuable tool for analyzing smartphone-captured images of skin lesions.
AI can complement the diagnostic knowledge of experienced dermatologists.
Advancements in AI and dermoscopy suggest breakthroughs in skin cancer diagnosis.

Conclusions:

AI holds significant potential to improve skin cancer diagnosis and management.
Challenges remain regarding technology accuracy and legal liabilities.
AI integration, alongside dermoscopy, is poised to advance the field of cutaneous oncology.