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The role of clinical competency-based education in training in radiation science: A scoping review
1The Mount Sinai Center for Radiation Sciences Education at Stony Brook University, Mount Sinai Health System, New York, NY, USA.
Background:
Clinical competency-based education (CBE) has emerged as a critical strategy to enhance workforce readiness in radiation sciences. Despite its growing adoption, significant gaps persist in standardized curricula, training strategies, and competency assessments across disciplines.
Purpose:
This scoping review aimed to (1) summarize best practices in training radiation science professionals; (2) examine clinical competency-based education (CBE) approaches in training radiation science trainees across disciplines including clinical radiation oncology and radiology, radiation therapy, medical dosimetry, and radiologic technology; and (3) synthesize evidence on learning and teaching strategies that support clinical competence development.
Methods:
Twenty clinical studies published between 2018 and 2023 were analyzed across four domains: best practices in professional training, competency-based educational frameworks, effective learning strategies, and teaching interventions. Studies were appraised for statistical outcomes, implementation design, and impact on learner performance or clinical readiness.
Results:
Key findings highlighted the effectiveness of interprofessional education, simulation-based training, virtual learning environments, and reflective practice in improving clinical competence. Studies also identified institutional and learner-level factors, including prior certification, gender, and postgraduate year, that influenced performance. Emerging technologies such as e-learning platforms and artificial intelligence (AI)-supported decision tools demonstrated positive effects on knowledge acquisition and skill transfer.
Conclusions:
The evidence supports integrating structured, competency-based frameworks and interactive learning modalities into radiation science education. Future directions should explore how artificial intelligence can further standardize assessments, personalize learning, and enhance clinical skill development.
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