Artificial intelligence (AI) is being increasingly used to automate and improve technologies
within the field of medical imaging. A critical step in the development of an AI algorithm is …

Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as
it will in everyday life. In this review, we focus on the potential applications of AI in the field …

An important need exists for strategies to perform rigorous objective clinical-task-based
evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need …

Despite major advances in artificial intelligence (AI) research for healthcare, the deployment
and adoption of AI technologies remain limited in clinical practice. This paper describes the …

Purpose Total metabolic tumor volume (TMTV) segmentation has significant value enabling
quantitative imaging biomarkers for lymphoma management. In this work, we tackle the …

Anomaly detection (AD) is a challenging problem in computer vision. Particularly in the field
of medical imaging, AD poses even more challenges due to a number of reasons, including …

Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …

The field of medicine is witnessing an exponential growth of interest in artificial intelligence
(AI), which enables new research questions and the analysis of larger and new types of …

Purpose The generalizability and trustworthiness of deep learning (DL)–based algorithms
depend on the size and heterogeneity of training datasets. However, because of patient …

This paper is the first of a Series on theranostics that summarises the current landscape of
the radiopharmaceutical sciences as they pertain to oncology. In this Series paper, we …