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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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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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Tumor Immunotherapy01:27

Tumor Immunotherapy

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Related Experiment Video

Updated: Aug 31, 2025

A 3D Organotypic Melanoma Spheroid Skin Model
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Artificial intelligence in melanoma: A systematic review.

Shu Zhang1, Yuanzhuo Wang1, Qingyue Zheng1

  • 1Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.

Journal of Cosmetic Dermatology
|August 24, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promising results in melanoma evaluation, diagnosis, and prediction. This systematic review highlights AI

Keywords:
artificial intelligencecancer managementdiagnosismachine learningmelanoma

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Area of Science:

  • Dermatology
  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Melanoma is a leading cause of skin cancer mortality.
  • Artificial intelligence (AI) applications in oncology are expanding, including recent advancements in melanoma research.
  • A systematic review was needed to consolidate AI's role in melanoma.

Approach:

  • A systematic literature search was conducted on the PubMed database.
  • The search query combined "artificial intelligence" and "melanoma."
  • Fifty-one relevant articles published up to August 1, 2020, were included.

Key Points:

  • AI techniques are primarily utilized for dermoscopic image evaluation, image segmentation, and processing.
  • AI contributes to the development of diagnostic systems for melanoma.
  • AI applications extend to predicting metastasis, drug response, and patient prognosis in melanoma.

Conclusions:

  • AI demonstrates satisfactory performance in melanoma-related tasks.
  • Future potential applications of AI in melanoma are vast and significant.
  • Patient perspectives and human-AI collaboration in melanoma care are emerging areas of interest.