Artificial intelligence (AI)-driven data-centric paradigms are catalyzing a paradigm shift in radiopharmaceutical development and molecular imaging, two pivotal technologies that underpin precision nuclear medicine. This review focuses on the cutting-edge applications of AI in radiopharmaceutical discovery and molecular image analytics, and systematically investigates the technical principles and typical cases of Deep Learning algorithms (e.g., Graph Neural Networks (GNNs), Generative Adversarial Networks (GANs), and Transformer Models) in target identification, ligand design, pharmacokinetic optimization, and image reconstruction and enhancement. By integrating multi-omics data and 3D structural information, AI can significantly improve the accuracy of target affinity prediction for radiopharmaceuticals and accelerate the design of novel ligands. In the field of molecular imaging, AI-driven low-dose single-photon emission computed tomography (SPECT) and positron emission tomography (PET) image reconstruction, tumor segmentation, and quantitative analysis techniques have significantly improved the diagnostic efficiency and accuracy, providing a reliable basis for individualized treatment. In addition, the paper discusses data privacy, model generalization, and ethical challenges faced by AI in clinical translation, and looks forward to the future direction of multidisciplinary integration (e.g., combining AI with radiochemistry and nuclear medicine) and technological innovations, which will help precision medicine leap from theory to practice.

Artificial intelligence  /  Radiopharmaceuticals  /  Molecular imaging  /  Precision medicine  /  Deep learning  /  Clinical translation  /  Image reconstruction  /  Multimodal data fusion