Generative Artificial Intelligence in Nuclear Medicine Education.

Abstract

Generative artificial intelligence (genAI) has become assimilated into the education, research, and clinical domains of nuclear medicine and health care. Understanding the principles, limitations, and applications of genAI is important for capitalizing on its transformative potential in student education and impact on sustainability within both the education and the clinical sectors. In this article, the fundamental principles and applications of artificial intelligence are explored from the context of nuclear medicine. GenAI technologies are defined and capabilities outlined. A detailed investigation of the potential and limitations of both text-to-text and text-to-image genAI based in empiric and anecdotal research is provided. Specific examples of applications of text-to-text and text-to-image genAI are provided. GenAI has the potential to reinvigorate nuclear medicine education by supporting and enriching student learning and to be transformative in nuclear medicine education, but at the time of writing, both text-to-text and text-to-image genAI are far from revolutionary. Nonetheless, the horizon promises transformative education applications of genAI. GenAI can enhance nuclear medicine education and student learning and provide economies to improve sustainability in the education and clinical sectors. Although there are some limitations to current capabilities, this rapidly evolving space will soon offer potential benefits to education.