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

Area of Science:

• Medical Education
• Nuclear Medicine Technology
• Artificial Intelligence in Healthcare

Background:

• Nuclear medicine (NM) training is evolving due to increased clinical volumes, new radiopharmaceuticals, and advanced imaging technologies.
• Artificial intelligence (AI) tools present new opportunities and challenges for NM education, offering assistance in study, content generation, and research.
• The potential for AI to generate inaccurate or biased information necessitates a disciplined approach to its use in learning environments.

Purpose of the Study:

• To provide practical AI workflows for nuclear medicine (NM) technology students, residents, and fellows.
• To emphasize the importance of self-regulated learning, professional judgment, accountability, and source verification when using AI tools.
• To guide learners in utilizing AI as a structured learning partner, rather than an unquestioned authority.

Main Methods:

• The article outlines learner-focused AI workflows, building on theoretical foundations.
• It emphasizes a structured approach integrating AI as a learning partner.
• Key strategies include goal setting, independent effort, misconception identification, retrieval practice, self-explanation, source grounding, faculty review, and iterative revision.

Main Results:

• While direct evidence on NM learner outcomes is limited, broader health professions literature supports cautious AI exploration.
• Learners must prioritize verification, critical evaluation, and feedback over passive answer generation from AI.
• Effective AI integration requires learners to maintain skepticism and avoid common pitfalls like sharing protected information or neglecting copyright.

Conclusions:

• AI can be a valuable tool in nuclear medicine (NM) education when used responsibly and critically.
• Learners must actively engage with AI, focusing on self-regulated learning and verification to ensure durable knowledge acquisition.
• Adherence to ethical guidelines, including disclosure of AI use and skepticism toward generated content, is crucial for effective and accountable learning.