Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering

TL;DR

This paper discusses how artificial intelligence and machine learning can revolutionize PET imaging by enhancing radiopharmaceutical design, synthesis, and radiolabeling processes, bridging research and clinical applications.

Contribution

It provides a perspective on applying AI/ML to improve radiopharmaceutical development and radiolabeling strategies in PET imaging.

Findings

AI/ML can optimize radiolabeling reactions

Potential to accelerate radiopharmaceutical development

Improves design and synthesis of PET tracers

Abstract

Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic. In this perspective we offer insights into how the technology could be applied to improve the design and synthesis of new radiopharmaceuticals for PET imaging, including identification of an optimal labeling approach as well as strategies for radiolabeling reaction optimization.