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AI-Empowered Nuclear Medicine Education, Part 2: Practical AI Applications for Educators
Justin G Peacock1,2, Keith M Jacobs3
1School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland; and justin.peacock@usuhs.edu.
Journal of Nuclear Medicine Technology
|August 4, 2026
Summary
Nuclear medicine (NM) educators can leverage artificial intelligence (AI) by integrating practical, theory-informed techniques into their workflow. This approach fosters ethical AI use and enhances training for evolving healthcare demands.
Area of Science:
- Medical Education
- Artificial Intelligence in Healthcare
- Nuclear Medicine Training
Background:
- Nuclear medicine (NM) education faces challenges from rapid scientific advancement, increased demand, and staff shortages.
- Artificial intelligence (AI) is increasingly prevalent, yet its application in health education, particularly NM, lacks practical guidance and ethical frameworks.
- Educators express uncertainty and fear regarding AI integration due to limited understanding and practical application strategies.
Purpose of the Study:
- To provide nuclear medicine educators with practical strategies for integrating AI into their teaching workflows.
- To ground AI implementation in established learning theories and ethical principles for effective and responsible use.
- To empower educators to model effective human-AI collaboration for learners in nuclear medicine.
Main Methods:
- The study outlines an educator-focused framework based on transformative and experiential learning theories.
- Practical activities are introduced to allow direct engagement with AI techniques like prompt engineering and retrieval-augmented generation.
- Readers are guided to reflect on the application and integration of AI tools within their specific educational contexts.
Main Results:
- The article details effective AI techniques applicable to NM education, including prompt engineering, chain-of-thought prompting, and retrieval-augmented generation.
- Practical activities demonstrate the benefits and potential challenges of using AI in educational settings.
- The approach emphasizes creating scalable, efficient, equitable, accurate, and personalized learning experiences through AI.
Conclusions:
- AI-enabled nuclear medicine education requires a foundation in learning theory and ethical considerations for successful integration.
- Practical engagement with AI tools and techniques empowers educators to guide learners in responsible human-AI interaction.
- Adopting AI in NM education is crucial for developing a scalable, adaptable, and effective training system to meet evolving practice demands.
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