Hooman H Rashidi, Joshua Pantanowitz, Mathew 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
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引用次数: 0
Abstract
This manuscript serves as an introduction to a comprehensive seven-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 non-generative (traditional) AI. Thereby serving as a primer to the other six 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 healthcare system.
期刊介绍:
Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology.
Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.