Malignant lymphomas encompass a range of malignancies with incidence rising globally, particularly with age. In younger populations, Hodgkin and Burkitt lymphomas predominate, while older populations more commonly experience subtypes such as diffuse large B-cell, follicular, marginal zone, and mantle cell lymphomas. Positron emission tomography/computed tomography (PET/CT) using [18F] fluorodeoxyglucose (FDG) is the gold standard for staging, treatment response assessment, and prognostication in lymphoma. However, interpretation of PET/CT is complex, time-consuming, and reliant on expert imaging specialists, exacerbating challenges associated with workforce shortages worldwide. Artificial intelligence (AI) offers transformative potential across multiple aspects of PET/CT imaging in this setting.

AI applications in appointment planning have demonstrated utility in reducing nonattendance rates and improving departmental efficiency. Advanced reconstruction techniques leveraging convolutional neural networks (CNNs) enable reduced injected activities of radiopharmaceutical and patient dose whilst maintaining diagnostic accuracy, particularly benefiting younger patients requiring multiple scans. Automated segmentation tools, predominantly using 3D U-Net architectures, have improved quantification of metrics such as total metabolic tumour volume (TMTV) and total lesion glycolysis (TLG), facilitating prognostication and treatment stratification. Despite these advancements, challenges remain, including variability in segmentation performance, impact on Deauville Score interpretation, and standardization of TMTV/TLG measurements. Emerging large language models (LLMs) also show promise in enhancing PET/CT reporting, converting free-text reports into structured formats, and improving patient communication.

Further research is required to address limitations such as AI-induced errors, physiological uptake differentiation, and the integration of AI models into clinical workflows. With robust validation and harmonization, AI integration could significantly enhance lymphoma care, improving diagnostic precision, workflow efficiency, and patient outcomes.