This scientific topic "Recent Developments in Artificial Intelligence and Radiomics" contains twelve studies that show new directions in artificial intelligence and radiomics.[6] In nuclear medicine, infection imaging has traditionally relied on visual interpretation with simple semi-quantitative indices, which is limited by subjectivity, lower sensitivity for subtle disease, and standardization issues.[1] Advances in quantitative imaging, radiomics and artificial intelligence, collectively called computomics, enable objective and reproducible characterization of diseases.[1] Quantitative imaging converts the tracer distribution into measurable biological metrics, such as the count ratio in the region of interest, standardized absorbance values, and kinetic parameters.[1] Radiomics extracts high-dimensional features of intensity, shape, texture, and spatial heterogeneity, revealing information invisible to the human eye.[1] Artificial intelligence using machine learning and deep learning integrates this data with clinical variables to automate segmentation, improve reconstruction, classification, and predictive modeling.[1] These tools help distinguish infection from sterile inflammation, quantify disease burden, monitor response to therapy, and standardize interpretation in complex cases.[1] Accuracy depends on acquisition, reconstruction, and processing parameters, so harmonization, standardization, and validation are needed.[1]