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Medical applications of generative adversarial network: a visualization analysis.

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Summary
This summary is machine-generated.

Generative adversarial networks (GANs) are increasingly used in medicine beyond image processing. These AI tools show promise for clinical applications like privacy protection and diagnosis, but require ethical validation.

Keywords:
CitespaceGenerative adversarial networkartificial intelligencevisualization analysis

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Area of Science:

  • Artificial Intelligence
  • Deep Learning
  • Generative Adversarial Networks (GANs)

Background:

  • Deep learning (DL) represents a cutting-edge AI approach.
  • Generative Adversarial Networks (GANs), an unsupervised DL method, excel at synthesizing novel data.
  • GANs are emerging as powerful tools in various scientific domains.

Purpose of the Study:

  • To investigate the diverse applications of GANs within the medical field.
  • To highlight the importance of GANs for advancing clinical medical research.
  • To conduct a visual bibliometric analysis of GANs in medicine using Citespace and statistical methods.

Main Methods:

  • Systematic literature search across PubMed, MEDLINE, Web of Science, and Google Scholar (2017-2022).
  • Study conducted adhering to PRISMA guidelines.
  • Bibliometric analysis using Citespace to assess publications, authors, institutions, and keywords.

Main Results:

  • GAN applications extend beyond medical image processing into broader and more complex medical fields.
  • GANs demonstrate potential for direct application in clinical medicine.
  • Identified trends in research output, key contributors, and prevalent research themes.

Conclusions:

  • GANs are extensively integrated into the medical field with expanding clinical utility.
  • Future applications are anticipated in privacy protection and medical diagnosis.
  • Ethical, legal considerations, and expert validation are crucial for clinical GAN deployment.