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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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Changes in Skin Color: Clinical Perspectives01:14

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The first thing a clinician sees is the skin, so the examination of the skin should be part of any thorough physical examination. Most skin disorders are relatively benign, but a few, including melanomas, can be fatal if untreated. A couple of the more noticeable disorders, albinism and vitiligo, affect the appearance of the skin and its accessory organs.
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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Basic principles of artificial intelligence in dermatology explained using melanoma.

Tim Hartmann1, Johannes Passauer1, Julien Hartmann2

  • 1Department of Dermatology, University hospital Tübingen, Tübingen, Germany.

Journal Der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG
|February 16, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in medicine is advancing rapidly. This review explains AI

Keywords:
Melanomaartificial intelligenceartificial neural networkmachine learningteledermatology

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Dermatology

Background:

  • Artificial intelligence (AI) adoption in medicine is accelerating, yet healthcare professionals often lack foundational technical understanding.
  • This knowledge gap hinders the effective clinical and research application of AI technologies.
  • Malignant melanoma presents a significant healthcare challenge, with early detection crucial for improved patient prognosis.

Purpose of the Study:

  • To elucidate the fundamental principles and classification of artificial intelligence (AI).
  • To utilize melanoma detection as a specific, illustrative example for understanding AI mechanisms.
  • To bridge the gap in understanding AI technology for clinical and research applications.

Main Methods:

  • Review of artificial intelligence (AI) concepts and classifications.
  • Detailed illustrations explaining AI technology.
  • Case study focusing on AI-driven melanoma recognition from medical images.

Main Results:

  • AI, particularly machine learning, demonstrates capability in identifying melanomas from images.
  • AI performance in melanoma detection can approach that of experienced dermatologists under specific conditions.
  • Elaborate illustrations aid in demystifying AI technology for a broader audience.

Conclusions:

  • A clear understanding of AI's functioning is essential for its successful integration into clinical practice.
  • AI offers a promising solution to address healthcare system burdens, such as the finite availability of dermatological experts.
  • This review provides a foundational understanding of AI, using melanoma as a key example, to facilitate its clinical adoption.