Rashidi Hooman H, Pantanowitz Joshua, Hanna Matthew G, Tafti Ahmad P, Sanghani Parth, Buchinsky Adam, Fennell Brandon, Deebajah Mustafa, Wheeler Sarah, Pearce Thomas, Abukhiran Ibrahim, Robertson Scott, Palmer Octavia, Gur Mert, Tran Nam K, Pantanowitz Liron
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.
School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
Mod Pathol. 2025 Apr;38(4):100688. doi: 10.1016/j.modpat.2024.100688. Epub 2025 Jan 3.
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.
本手稿是关于人工智能(AI)和机器学习(ML)及其在病理学和医学中的当前和未来影响的7部分综合综述文章系列的引言。这篇引言性综述全面介绍了这个快速发展的领域及其改变医学诊断、工作流程、研究和教育的潜力。使用广泛的词典涵盖了AI-ML中使用的基本术语。本文还对AI-ML领域的主要领域进行了广泛概述,包括生成式和非生成式(传统)AI,从而作为本系列其他6篇综述文章的入门介绍,这些文章描述了AI-ML中的统计、法规、偏差、伦理困境和ML-Ops的详细信息。这些综述文章的目的是更好地武装那些正在或将要在人工智能支持的医疗保健系统中工作的人员。