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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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Clinical Applications of Epidermal Stem Cells01:19

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Epidermal stem cells (EpiSCs) are mainly located at the basal layer of the epidermis. These cells repair minor injuries of the skin and replace dead skin cells. However, EpiSCs’ cannot heal severe wounds such as major burns or those from diabetes or hereditary disorders. In such cases, culturing the epidermal stem cells from the patient is possible and has yielded successful treatment options, such as laboratory-grown skin grafts. These grafts are synthesized using a patient’s own...
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Updated: Oct 9, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Machine Learning and Its Application in Skin Cancer.

Kinnor Das1, Clay J Cockerell2,3, Anant Patil4

  • 1Department of Dermatology Venereology and Leprosy, Silchar Medical College, Silchar 788014, India.

International Journal of Environmental Research and Public Health
|December 24, 2021
PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) offer promising tools for early skin cancer detection. These technologies can aid in diagnosing skin cancer efficiently, improving patient outcomes and reducing healthcare burdens.

Keywords:
artificial intelligencemachine learningskin cancer

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Dermatology

Background:

  • Artificial intelligence (AI) is increasingly applied in healthcare, with significant potential in dermatology.
  • Machine learning (ML), a subset of AI, uses algorithms to learn from data for prediction and task performance.
  • Dermatology's adoption of AI lags behind other fields like radiology, despite AI's growing accessibility.

Purpose of the Study:

  • To explore the fundamentals of machine learning (ML).
  • To discuss the potential of ML in assisting with the diagnosis of skin cancer.
  • To highlight the need for automated diagnostic systems in dermatology due to specialist limitations.

Main Methods:

  • Review of machine learning (ML) principles and applications in medical image analysis.
  • Discussion of deep convolutional neural networks for skin lesion evaluation.
  • Exploration of AI's role in early disease detection.

Main Results:

  • AI and ML show significant promise for the early detection of skin cancer.
  • Automated systems using ML can efficiently diagnose skin cancer, complementing specialist expertise.
  • Deep convolutional neural networks can be developed into systems for skin image analysis and diagnosis.

Conclusions:

  • Machine learning (ML) is a valuable tool for enhancing skin cancer diagnosis.
  • AI-powered systems can improve diagnostic efficiency, leading to better patient outcomes.
  • Wider adoption of AI in dermatology is crucial for addressing diagnostic challenges and reducing patient burdens.