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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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Updated: Aug 16, 2025

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

Published on: July 11, 2025

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Artificial intelligence in Dermatopathology.

Shishira R Jartarkar1, Clay J Cockerell2, Anant Patil3

  • 1Department of Dermatology, Vydehi Institute of Medical Sciences and Research Centre University-RGUHS, Bengaluru, India.

Journal of Cosmetic Dermatology
|December 22, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) aids dermatopathology diagnosis, leveraging digital microscopy and deep learning. Expert guidance is crucial for AI

Keywords:
Dermatopathologyartificial intelligencetechnology

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Area of Science:

  • Digital pathology and medical imaging analysis.
  • Application of artificial intelligence in healthcare.

Background:

  • Digital microscopy is transforming pathology into a digitally-oriented specialty.
  • Artificial intelligence (AI) offers significant potential to assist in dermatopathology diagnoses.

Purpose of the Study:

  • To review the current applications of AI in dermatopathology.
  • To discuss patient and clinician attitudes towards AI in this field.
  • To explore the challenges, limitations, and future opportunities for AI in dermatopathology.

Main Methods:

  • A comprehensive literature search was conducted using PubMed and Google Scholar.
  • Only English-language articles were included in the review.

Main Results:

  • Convolutional neural networks (CNNs), a type of deep neural network, excel in image recognition, processing, classification, and segmentation.
  • AI implementation in tumor pathology aids in diagnosis, grading, staging, prognostic prediction, and identifying pathological features.

Conclusions:

  • AI holds promise for enhancing dermatopathology diagnostics.
  • The effective integration of AI requires dermatopathologist expertise and guidance.
  • Addressing challenges and limitations is key to future AI implementation in dermatopathology.