Simulation-Enhanced Learning in Nuclear Medicine: Counterpoint

Simulation-based education has become widely embedded in health professional training and is increasingly positioned as a solution to clinical placement capacity constraints, patient safety imperatives, and growing procedural complexity. Yet the rapid expansion of simulation in nuclear medicine technologist education carries under-recognized risks. Evidence frequently demonstrates gains in confidence, knowledge, and short-term performance; however, transfer to workplace capability and sustained clinical outcomes is variable and depends strongly on program design, educator expertise, debriefing quality, and integration with authentic clinical experience. When misaligned with task demands, simulation can generate false confidence, fragile competence, inequitable access, and educational "theatre" that is expensive but difficult to justify. Emerging layers such as virtual reality and artificial intelligence add new implementation, safety, privacy, and bias concerns, including cybersickness, usability barriers, data governance risks, and unreliable automated feedback. In nuclear medicine, where errors can create patient harm, radiation safety incidents, workflow disruption, and learner psychological harm, simulation must be treated as a high-stakes educational technology requiring strong governance, rigorous evaluation, and explicit limits. This counterpoint argues that simulation should be framed as a disciplined adjunct to supervised workplace learning rather than a scalable substitute. The value of simulation is maximized when focused on targeted competencies, delivered under best-practice standards, and evaluated against defensible outcomes beyond learner satisfaction and confidence.

Authors

Institutions

Publication Details

Journal
Journal of Nuclear Medicine Technology
Published
2026-06-09
DOI
https://doi.org/10.2967/jnmt.125.271963
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Simulation-Enhanced Learning in Nuclear Medicine: Counterpoint

Geoffrey M. Currie
Journal of Nuclear Medicine Technology
Medical Imaging Techniques and Applications
article

Simulation-Enhanced Learning in Nuclear Medicine: Counterpoint

Geoffrey M. Currie
article en

Abstract

Simulation-based education has become widely embedded in health professional training and is increasingly positioned as a solution to clinical placement capacity constraints, patient safety imperatives, and growing procedural complexity. Yet the rapid expansion of simulation in nuclear medicine technologist education carries under-recognized risks. Evidence frequently demonstrates gains in confidence, knowledge, and short-term performance; however, transfer to workplace capability and sustained clinical outcomes is variable and depends strongly on program design, educator expertise, debriefing quality, and integration with authentic clinical experience. When misaligned with task demands, simulation can generate false confidence, fragile competence, inequitable access, and educational "theatre" that is expensive but difficult to justify. Emerging layers such as virtual reality and artificial intelligence add new implementation, safety, privacy, and bias concerns, including cybersickness, usability barriers, data governance risks, and unreliable automated feedback. In nuclear medicine, where errors can create patient harm, radiation safety incidents, workflow disruption, and learner psychological harm, simulation must be treated as a high-stakes educational technology requiring strong governance, rigorous evaluation, and explicit limits. This counterpoint argues that simulation should be framed as a disciplined adjunct to supervised workplace learning rather than a scalable substitute. The value of simulation is maximized when focused on targeted competencies, delivered under best-practice standards, and evaluated against defensible outcomes beyond learner satisfaction and confidence.

Journal of Nuclear Medicine Technology
Charles Sturt University (AU)
Openalex Percentile: Top 6%
Medical Imaging Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.