Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of radiomics and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

翻译：人工智能(AI)技术具有巨大潜力,能够有效、稳健和自动化的图像描述,包括确定微妙的形态。基于AI的探测探测探索图像空间,以根据模式和特征找到感兴趣的区域。有一系列肿瘤症状,从良性到恶性,可以通过AI的分类方法使用图像特征加以识别。从图像中提取可移动的信息,让位于放射学领域,并通过直线(手制/设计)和深放射学框架加以探索。放射学分析有可能作为一种非侵入性技术,用于准确定性肿瘤,以改进诊断和治疗监测。这项工作审查了基于AI的技术,特别侧重于病理PET和PET/CT成像,以完成不同的检测、分类和预测/预测/预测任务。我们还讨论了为使AI技术转化为常规临床工作流程而需作出的努力,以及可能作出的改进和补充技术,例如将自然语言处理用于电子健康记录和神经对口的AI技术。