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.
Keith M. Jacobs, J. Peacock· Journal of Nuclear Medicine...· 0 citations
Nuclear medicine (NM) is rapidly expanding with new radiopharmaceuticals, imaging equipment, and theranostic possibilities, necessitating a capable and competent workforce expansion. Consequently, NM education must undergo a transformation, powered in part by artificial intelligence (AI). AI is rapidly entering NM education through learner study materials, assessment design, simulation, feedback, and administrative workflows. AI’s value depends less on tool novelty than on ethical alignment with effective learning principles. NM educators and trainees need practical approaches for AI tool use that preserve human judgment, accountability, critical thought, creativity, privacy, and source verification. As the first article in a 3-part series on AI-empowered NM education, this practical guide summarizes and integrates selected literature from AI in health professions education, NM education, learning theories, and ethics. Building on a framework of established learning theories (transformative learning, self-regulated learning, experiential learning, connectivism, constructivism, and cognitive load theory) and ethical concerns about AI (e.g., accuracy, bias, transparency, privacy), we describe effective AI techniques (structured prompt design, retrieval-augmented generation, meta-prompting, AI customization, vibe coding) that can be applied for the NM educator and learner in parts 2 and 3 of the series, respectively. These benefits require explicit safeguards, including AI guardrail incorporation, privacy protection, expert review, AI output transparency, and healthy skepticism. AI should be integrated into NM education through theory-informed, source-grounded, and expert-reviewed workflows. Meeting the demands of increased NM volumes and expanded professional opportunities will require AI-empowered educators and trainees that are able to self-regulate and transform their learning through healthy patterns of human–AI interaction.
Keith M. Jacobs, J. Peacock· Journal of Nuclear Medicine...· 0 citations
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