Medicine and artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of terminology that has a specific way in which it is applied. However, when two distinct fields with overlapping terminology start to collaborate, miscommunication and misunderstandings can occur. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical AI contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. Then the data sets used for AI evaluation are classified, namely random splitting, cross-validation, temporal, geographic, internal, and external sets. The accurate and standardized description of these data sets is crucial for demonstrating the robustness and generalizability of AI applications in medicine. This review clarifies existing literature to provide a comprehensive understanding of these classifications and their implications in AI evaluation. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion. Among these solutions are the use of standardized terminology such as 'training set,' 'validation (or tuning) set,' and 'test set,' and explicit definition of data set splitting terminologies in each medical AI research publication. This review aspires to enhance the precision of communication in medical AI, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

医学和人工智能工程代表了两个不同的领域，每个领域都有数十年的出版历史。两个有重叠术语的不同领域合作时，可能会出现沟通不畅和误解。本文通过考察历史文献，包括文章、写作指南和教材，追溯数据集术语的分歧演化及其影响。通过明确这些数据集的准确和标准化描述，可以展示人工智能应用在医学中的健壮性和普适性。本文还识别了常被误解的术语，并提出了解决方案以减少术语混淆。为了提高医学人工智能中的沟通准确性，本综述旨在促进这个交叉学科领域更有效和透明的研究方法。

人工智能医学数据集术语：历史回顾与建议