Toward Integrated Clinical-Computational Nuclear Medicine

Area of Science:

• Nuclear Medicine
• Medical Informatics
• Computational Imaging

Background:

• Clinical-computational nuclear medicine is rapidly advancing.
• Key technologies include artificial intelligence (AI), tracer kinetic modeling, radiomics, and integrated informatics.

Purpose of the Study:

• To highlight key computational innovations in nuclear medicine.
• To emphasize the importance of clinical oversight in adopting these technologies.

Main Methods:

• Review of advancements in AI, tracer kinetic modeling, radiomics, and informatics.
• Discussion of applications in imaging quality, lesion detection, and personalized therapy.
• Exploration of workflow automation and Natural Language Processing (NLP).

Main Results:

• AI and computational tools improve imaging quality and automate lesion detection.
• Physiologically based pharmacokinetic modeling and voxel-level dosimetry enable personalized radiopharmaceutical therapy.
• Workflow automation and NLP enhance operational efficiency.

Conclusions:

• Computational innovations are transforming nuclear medicine towards precision imaging and therapy.
• Clinical oversight is essential for ensuring accuracy, interpretability, and patient safety.
• Clinician-guided evaluation is critical for the future development and adoption of these technologies.