胰腺癌是全球第12种最常见的癌症，总体生存率为4.9%。及时诊断胰腺癌对于治疗和生存至关重要。人工智能（AI）提供先进的模型和算法，以更好地诊断胰腺癌。本研究旨在探讨文献报道中用于预测和早期诊断胰腺癌的AI模型。本研究采用PRISMA-ScR指南（系统评价和Meta分析-排列扩展）进行范围检视。搜索PubMed、谷歌学术、Science Direct、BioRXiv和MedRxiv以识别相关文章。由两名评价人员独立进行研究选择和数据提取。从包括的研究中提取的数据进行叙述性综合。在1185篇出版物中，共有30项研究被纳入范围检视。包括文章报道了AI用于6个不同目的。在这些包含的文章中，AI技术主要用于胰腺癌的诊断（14/30，47%）。放射学图像（14/30，47%）是被包括在文章中使用频率最高的数据。大多数包括的文章使用小于1000个样本大小的数据集（11/30，37%）。深度学习模型是研究中用于胰腺癌诊断最显著的AI分支，卷积神经网络是最常用的算法（18/30，60%）。在包括的研究中使用了6种验证方法，其中最常用的方法是K倍交叉验证（10/30，33%）和外部验证（10/30，33%）。使用支持向量机、决策树和K均值聚类算法的研究中发现更高的准确性（99%）。本综述总结了基于AI模型和算法用于预测和诊断胰腺癌患者的研究。AI有望在推进胰腺癌预测和诊断方面发挥至关重要的作用。需要进一步研究提供支持临床决策的数据。©Zainab Jan、Farah El Assadi、Alaa Abd-alrazaq、Puthen Veettil Jithesh。原始刊物发表于《医学互联网研究杂志》（https://www.jmir.org），2023年3月31日。

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.©Zainab Jan, Farah El Assadi, Alaa Abd-alrazaq, Puthen Veettil Jithesh. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.03.2023.