病理学和医学中的人工智能（AI）和机器学习（ML）导论：生成和非生成AI基础。

IF 7.1 1区医学 Q1 PATHOLOGY

Modern Pathology Pub Date : 2025-01-03 DOI:10.1016/j.modpat.2024.100688

Hooman H. Rashidi , Joshua Pantanowitz , Matthew G. 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

摘要

该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综合七部分综述文章系列的介绍。这篇介绍性的综述提供了对这一快速扩展领域的全面把握，以及它在改变医疗诊断、工作流程、研究和教育方面的潜力。AI-ML中使用的基本术语使用广泛的字典进行介绍。本文还提供了AI- ml领域主要领域的广泛概述，包括生成和非生成（传统）AI。因此，作为本系列中其他六篇评论文章的入门文章，这些文章描述了AI-ML中的统计数据、法规、偏见、道德困境和ML-Ops的细节。这些评论文章的目的是更好地装备那些正在或将要在支持人工智能的医疗保健系统中工作的个人。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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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来源期刊

Modern Pathology 医学-病理学

CiteScore

14.30

自引率

2.70%

发文量

174

审稿时长

18 days

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.