Thoracic imaging has become an integral part of clinical care for patients with acute and chronic respiratory and cardiac conditions. This care includes a broad range of services, from population-based public health efforts like lung cancer screening to highly sophisticated, individualized approaches that allow providers to leverage specific clinical images to guide a single patient’s treatment. Artificial intelligence (AI) is catalyzing a paradigm shift in how thoracic imaging is applied in clinical practice. It supports disease detection, risk stratification, prognostication, and assessment of treatment response, reinforcing its evolving role in precision medicine (Wang et al. Front Radiol 1:781868, 2021; Mei et al. Nat Med 26:1224–1228, 2020; Miller et al. Nat Commun 15:2747, 2024). Additionally, artificial intelligence (AI) is progressively transforming functional imaging by increasing the accuracy and capabilities of established techniques. Through advanced data analysis, AI facilitates the identification of previously unrecognized relationships within datasets derived from dynamic imaging processes, including temporal and spectral acquisitions processes based on temporal and spectral imaging. These capabilities offer new opportunities to characterize physiological changes and disease progression with greater fidelity. This chapter will examine AI’s current contributions to thoracic imaging, particularly its quantitative applications, and discuss how these advancements may further integrate medical imaging into precision medicine in the coming decades.