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Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative Artificial Intelligence Basics

  • Hooman H. Rashidi*
  • , Joshua Pantanowitz
  • , Matthew G. Hanna
  • , Ahmad P. Tafti
  • , Parth Sanghani
  • , Adam Buchinsky
  • , Brandon Fennell
  • , Mustafa Deebajah
  • , Sarah Wheeler
  • , Thomas Pearce
  • , Ibrahim Abukhiran
  • , Scott Robertson
  • , Octavia Palmer
  • , Mert Gur
  • , Nam K. Tran
  • , Liron Pantanowitz
  • *Corresponding author for this work
  • University of Pittsburgh
  • University of California at San Francisco
  • Cleveland Clinic Foundation
  • University of California at Davis

Research output: Contribution to journal › Review article › peer-review

72 Citations (Scopus)

Abstract

This manuscript serves as an introduction to a comprehensive 7-part review article series on artificial intelligence (AI) and machine learning (ML) and their current and future influence within pathology and medicine. This introductory review provides a comprehensive grasp of this fast-expanding realm and its potential to transform medical diagnosis, workflow, research, and education. Fundamental terminology employed in AI-ML is covered using an extensive dictionary. The article also provides a broad overview of the main domains in the AI-ML field, encompassing both generative and nongenerative (traditional) AI, thereby serving as a primer to the other 6 review articles in this series that describe the details about statistics, regulations, bias, ethical dilemmas, and ML-Ops in AI-ML. The intent of these review articles is to better equip individuals who are or will be working in an AI-enabled health care system.

Original languageEnglish
Article number100688
JournalModern Pathology
Volume38
Issue number4
DOIs
Publication statusPublished - Apr 2025

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • ChatGPT
  • artificial intelligence
  • generative AI
  • generative pretrained transformer
  • machine learning
  • supervised & unsupervised ML

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