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

Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
PMID: 39755237

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.

Authors

  • Hooman H Rashidi
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Electronic address: rashidihh@upmc.edu.
  • Joshua Pantanowitz
    Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Matthew G Hanna
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Ahmad P Tafti
    School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA.
  • Parth Sanghani
    Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Adam Buchinsky
    Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Brandon Fennell
    Department of Medicine, UCSF, School of Medicine, San Francisco, California.
  • Mustafa Deebajah
    Pathology & Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio.
  • Sarah Wheeler
    Department of Pathology, University of Pittsburgh School of Medicine and UPMC, Pittsburgh, PA, United States.
  • Thomas Pearce
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania.
  • Ibrahim Abukhiran
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania.
  • Scott Robertson
    Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States.
  • Octavia Palmer
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Mert Gur
    Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Mechanical Engineering, Istanbul Technical University, Istanbul, Turkey.
  • Nam K Tran
    Dept. of Pathology and Laboratory Medicine, United States. Electronic address: nktran@ucdavis.edu.
  • Liron Pantanowitz
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.

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