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AI applications in renal pathology.

Yuankai Huo1, Ruining Deng1, Quan Liu1

  • 1Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA.

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Artificial intelligence (AI) is revolutionizing renal pathology, but requires interdisciplinary collaboration. This review explores current and future AI applications in renal pathology, emphasizing shared understanding between computer scientists and pathologists.

Keywords:
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Area of Science:

  • Digital pathology
  • Artificial intelligence in medicine
  • Renal pathology

Background:

  • AI, particularly deep learning, is rapidly advancing healthcare applications.
  • Successful AI deployments in digital pathology are driving new uses in renal pathology.
  • Effective AI integration in renal pathology necessitates interdisciplinary collaboration.

Purpose of the Study:

  • To provide an integrated review of AI applications in renal pathology.
  • To bridge the understanding gap between computer scientists and renal pathologists.
  • To explore current and future AI-driven advancements in the field.

Main Methods:

  • Review of standard stages in full-stack AI-assisted renal pathology studies (data collection to analysis).
  • Introduction of representative AI techniques optimized for renal pathology.
  • Examination of current clinical AI applications and future prospects.

Main Results:

  • AI applications in renal pathology are emerging, building on digital pathology successes.
  • Understanding the full AI pipeline, from data to analysis, is crucial.
  • Specific AI techniques tailored for renal pathology are being developed.

Conclusions:

  • Synergistic development in AI-assisted renal pathology depends on close collaboration.
  • Computer scientists must recognize AI's specific translatability to renal pathology.
  • Renal pathologists need to grasp core AI principles for effective integration.