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Introduction to Generative Artificial Intelligence: Contextualizing the Future.

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Generative artificial intelligence (GAI) offers significant potential for pathology, but requires careful management of risks and biases. Understanding GAI's development and implementation is crucial for pathologists to lead in diagnostic medicine.

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Area of Science:

  • Pathology
  • Artificial Intelligence
  • Health Informatics

Background:

  • Generative artificial intelligence (GAI) is rapidly evolving, presenting both opportunities and challenges for healthcare professionals.
  • Pathologists face difficulties keeping pace with GAI advancements, terminology, and technical aspects.
  • Understanding GAI is essential for recognizing its potential risks and impacts on pathology practice.

Purpose of the Study:

  • To provide pathologists with accessible information on GAI development, evaluation, and implementation.
  • To highlight the relevance of GAI to laboratory applications and clinical practice.

Main Methods:

  • Literature review of recent studies and reviews.
  • Information sourced from PubMed and arXiv databases.

Main Results:

  • GAI presents numerous potential benefits for pathologists.
  • Clinical use of GAI necessitates rigorous oversight and continuous refinement.
  • GAI performance is contingent on data quality and diversity, with risks of bias propagation.
  • Ethical considerations, including patient privacy and autonomy, are paramount for responsible GAI deployment.

Conclusions:

  • Pathologists can leverage GAI to enhance diagnostic medicine.
  • Addressing ethical concerns and managing data quality are critical for successful GAI integration.
  • Continuous learning and adaptation are key for pathologists to remain leaders in the field.