Toward Integrated Clinical-Computational Nuclear Medicine

Faraz Farhadi; Shadi A Esfahani; Fereshteh Yousefirizi; Monica Luo; Pedro Esquinas Fernandez; Arkadiusz Sitek; Hamid Sabet; Babak Saboury; Arman Rahmim; Pedram Heidari

doi:10.1016/j.cpet.2025.09.003

Toward Integrated Clinical-Computational Nuclear Medicine

PET Clin. 2026 Jan;21(1):17-31. doi: 10.1016/j.cpet.2025.09.003. Epub 2025 Oct 25.

Authors

Affiliations

¹ Department of Radiology, Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, 55 Fruit Street, White 427, Boston, MA 02114, USA; Department of Nuclear Oncology, Institute of Nuclear Medicine, Bethesda, MD 20815, USA.
² Department of Radiology, Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, 55 Fruit Street, White 427, Boston, MA 02114, USA.
³ Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada.
⁴ Department of Nuclear Oncology, Institute of Nuclear Medicine, Bethesda, MD 20815, USA; Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Bethesda, MD 20892, USA.
⁵ Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Physics & Astronomy, University of British Columbia, 325 - 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada.
⁶ Department of Radiology, Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, 55 Fruit Street, White 427, Boston, MA 02114, USA. Electronic address: heidari.pedram@mgh.harvard.edu.

PMID: 41284557
DOI: 10.1016/j.cpet.2025.09.003

Abstract

The field of clinical-computational nuclear medicine is rapidly advancing, fueled by artificial intelligence, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This article highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.

Keywords: Artificial intelligence (AI); Computational nuclear medicine; Natural language processing (NLP); Personalized dosimetry; Predictive modeling; Radiomics; Radiopharmaceutical therapy (RPT); Workflow automation.

Publication types

Review

MeSH terms

Artificial Intelligence
Humans
Nuclear Medicine* / methods
Nuclear Medicine* / trends
Precision Medicine
Radiopharmaceuticals

Substances

Radiopharmaceuticals

Grants and funding

K08 CA259626/CA/NCI NIH HHS/United States