Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in...

Prostate-specific membrane antigen positron emission tomography (PSMA-PET) has become pivotal in prostate cancer (PCa) management, offering superior sensitivity over conventional imaging for detecting tumors, metastases, and biochemical recurrence. However, interpretive subjectivity, workflow inefficiencies, and heterogeneous PSMA expression remain significant limitations. Artificial intelligence (AI), particularly radiomics and deep learning, addresses these challenges by enabling automated lesion analysis and image enhancement. This review examines the impact of AI across the PSMA-PET workflow, covering optimized image acquisition (e.g., low-dose protocols, motion correction), enhanced interpretation (e.g., lesion characterization, prognostic stratification), and personalized theranostics (e.g., treatment response forecasting, radioligand therapy dosimetry). Despite promising multicenter validation, challenges remain in annotation standardization, data heterogeneity, model generalizability, interpretability, regulatory integration, and ethics. We further discuss emerging frontiers, including multimodal multi-omic integration, generative AI, and AI-driven clinical decision support systems. Notably, we highlight the evolving role of nuclear medicine physicians and radiologists as integrators of AI-derived biomarkers, who validate AI outputs for high-stakes decisions, retain interpretive authority for complex cases, and oversee quality assurance, ensuring that AI augments rather than replaces specialist expertise. These advances position AI-integrated PSMA-PET to drive precision oncology, with key pathways outlined for clinical translation and future innovation in PCa care.