AI-Empowered Nuclear Medicine Education, Part 1: Theoretical and Ethical Foundation
TL;DR
Effective AI techniques are described that can be applied to meet the demands of increased NM volumes and expanded professional opportunities, and require explicit safeguards, including AI guardrail incorporation, privacy protection, expert review, AI output transparency, and healthy skepticism.
Abstract
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.