AI-Empowered Nuclear Medicine Education, Part 3: Practical AI Applications for Learners

Abstract

Expanding clinical volumes, evolving radiopharmaceuticals, and new imaging technologies shape the training environment for nuclear medicine (NM) learners. Additionally, artificial intelligence (AI) tools are increasingly available. These tools can draft study guides, generate questions, simulate patient interactions, critique explanations, organize sources, and assist early research development. However, these benefits require disciplined use, because AI tools can also generate inaccurate, biased, unsupported, or overly fluent responses that interfere with durable learning. This learner-focused article provides practical AI workflows for NM technology students, residents, and fellows. It emphasizes preserving self-regulated learning, professional judgment, accountability, and source verification. Building on the theoretical foundations of part 1, this article treats AI as a structured learning partner rather than an educational authority. Direct evidence regarding NM learner outcomes remains limited. Nevertheless, broader health professions literature supports cautious AI exploration when learners prioritize verification, critical evaluation, and feedback over passive answer generation. Effective workflows require goal setting, initial independent effort, misconception identification, retrieval practice, self-explanation, source grounding, faculty review, and iterative revision. Furthermore, learners must avoid protected information, respect copyright, disclose AI assistance, and maintain skepticism toward generated output.