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 D. Fennell, Mustafa Deebajah, Sarah Wheeler, Thomas M. Pearce, Ibrahim Abukhiran, Scott Robertson, Octavia M. Peck Palmer, Mert Gur, Nam K. Tran, Liron Pantanowitz
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.