Artificial intelligence (AI) has vast potential to reshape nuclear medicine. Applications can be found at every step of the processing workflow, including image acquisition, image reconstruction, enhancement, and registration, segmentation, extraction of image-derived biomarkers, and prognostication, both for clinical and preclinical research and routine use. Emerging perspectives include computational nuclear oncology, foundation models, biomorphic AI, and quantum AI - currently exploratory but rapidly evolving. However, clinical translation remains limited. This narrative review summarizes the current literature on the subject and highlights current developments and future research directions. As such, it is not intended as a systematic analysis; instead, it outlines potential future directions, supported by multiple examples. Analysis of literature reveals impressive advances in the application of AI to nuclear medicine, such as improved time resolution, robust denoising, and automated lesion segmentation. Key challenges include the need for high-quality reference data, generalizability across scanners and populations, transparency and uncertainty quantification of model decisions, and compliance with evolving regulatory frameworks. For the future, progress will require broad collaboration between AI developers from research and industry, clinicians, hospital information technology, and regulators, while structured initiatives such as joint symposia should actively include patients to ensure innovations address real needs. Education across disciplines will also be critical for building trust and competence towards fully embracing the potential of AI for better (nuclear) medicine.
Kaiser et al. (Fri,) studied this question.
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