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Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative
Hooman H Rashidi1, Joshua Pantanowitz2, Matthew G Hanna1
1Department 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.
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.
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