Towards Integrated Clinical-Computational Nuclear Medicine

TL;DR

This paper reviews recent advances in clinical-computational nuclear medicine, emphasizing AI, modeling, and informatics to improve imaging, therapy, and operational workflows, while stressing the importance of clinical oversight.

Contribution

It highlights key computational innovations and underscores the critical role of clinician-guided evaluation in advancing precision nuclear medicine.

Findings

AI and radiomics enhance imaging quality and lesion detection

Physiologically based pharmacokinetic modeling enables personalized therapy

Workflow automation and NLP improve operational efficiency

Abstract

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