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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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Related Experiment Video

Updated: Jun 22, 2025

The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal Skin and Melanoma Progression
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Artificial intelligence and skin melanoma.

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  • 1Dermatology Department, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom.

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|June 28, 2024
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Summary
This summary is machine-generated.

Artificial intelligence (AI) aids in melanoma management by distinguishing cancerous lesions from benign ones using dermatoscopy images. An AI medical device (AIaMD) tool is used in UK practice, improving melanoma diagnosis and treatment planning.

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Area of Science:

  • Dermatology
  • Medical Artificial Intelligence (AI)
  • Oncology

Background:

  • Melanoma, the deadliest skin cancer, presents diagnostic challenges due to common benign pigmented lesions.
  • Timely diagnosis and treatment are critical, but removing all lesions is impractical.
  • Dermatoscopy aids in differentiating melanoma from benign lesions.

Purpose of the Study:

  • To discuss the role of AI in melanoma management.
  • To describe an AI-as-a-medical-device (AIaMD) tool utilized in UK clinical practice.
  • To explore the scope, risks, and mitigations for future AI implementation in melanoma care.

Main Methods:

  • Development of an AIaMD tool using dermatoscopy images with confirmed diagnoses.
  • Clinical implementation of the AIaMD tool in UK healthcare settings.
  • Analysis of AIaMD tool performance and impact on patient management.

Main Results:

  • The AIaMD tool has been used in current UK clinical practice on over 80,000 patients.
  • AI assists in distinguishing between melanoma and benign pigmented lesions.
  • Data from extensive use provides a basis for future AI development and deployment.

Conclusions:

  • AI demonstrates significant potential in enhancing melanoma diagnosis and management.
  • The discussed AIaMD tool is a validated resource in current clinical practice.
  • Further exploration of AI's scope, risks, and mitigation strategies is essential for widespread adoption.