Applications of artificial intelligence in nuclear medicine.
Authors
Affiliations (12)
Affiliations (12)
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Munich, Germany. Electronic address: [email protected].
- Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Augsburg, Germany; Digital Medicine, University Hospital Augsburg, Neusaess, Germany; School of Computation, Information and Technology, Technical University of Munich, Garching bei München, Germany.
- Medical Physics and Biophysics, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.
- Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany; Partner Site Regensburg, Bavarian Cancer Research Center, Regensburg, Germany; Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany.
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich, Jülich, Germany; Department of Nuclear Medicine, RWTH Aachen University Hospital, Aachen, Germany.
- Department of Nuclear Medicine, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany.
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair for Clinical Nuclear Medicine, 91054 Erlangen, Germany.
- DIGIT-X Lab, Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany.
- Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
- Department of Nuclear Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Abstract
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.