Artificial Intelligence (AI) is revolutionising nuclear medicine, offering unprecedented opportunities to enhance image quality, improve diagnostic accuracy, and optimise treatment planning. This chapter provides a comprehensive overview of AI's current applications and prospects in nuclear medicine. We begin by exploring AIassisted image reconstruction and analysis techniques, which enable the generation of high-quality images from lower radiation doses and facilitate more efficient and accurate image interpretation. Applying machine learning algorithms, including supervised, unsupervised, and deep learning approaches, to improve nuclear medicine diagnostics is discussed in detail. These AI-driven methods demonstrate remarkable capabilities in lesion detection, image classification, and quantitative analysis across various atomic medicine modalities. The chapter further delves into the role of AI in predictive modelling for treatment planning, examining how these technologies enable more personalised approaches to therapy. This includes using radiomics for treatment response prediction and AI-driven dosimetry optimisation in targeted radionuclide therapies. Despite the significant advancements, the integration of AI in nuclear medicine faces several challenges, including data quality and standardisation, model interpretability, clinical validation, and ethical considerations. The chapter concludes by discussing these challenges and exploring future directions in the field, including emerging technologies such as federated learning, AI-driven tracer development, and the potential applications of quantum computing. By providing a balanced view of both the opportunities and challenges, this chapter aims to give readers a comprehensive understanding of the transformative potential of AI in nuclear medicine and its implications for improving patient care.
Keywords: Artificial intelligence, nuclear medicine, machine learning, radiomics, predictive modelling.
