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A Practice-Aligned Approach to Integrating AI in Radiation Sciences Education.

This article focuses on the integration of artificial intelligence (AI) into radiation sciences education at the Mount Sinai Center for Radiation Sciences Education at Stony Brook University. It describes a multidimensional, practice-aligned approach embedding AI tools—including large language model...

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Published in:Radiologic Technology Vol. 97; no. 5; pp. 320 - 329
Main Authors: McDonagh, Danielle, Olsen, Victoria, Machuca, Anthony, Dumane, Vishruta, Prando, Matias, Dimopoulos, Maria P.
Format: diagnostic images pictorial tables/charts Journal Article
Published: American Society of Radiologic Technologists May/Jun2026
Online Access:View this record in EBSCOhost
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      dt: May/Jun2026
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      place: Alburquerque, New Mexico
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        atl: A Practice-Aligned Approach to Integrating AI in Radiation Sciences Education.
      aug:
        au:
          McDonagh, Danielle
          Olsen, Victoria
          Machuca, Anthony
          Dumane, Vishruta
          Prando, Matias
          Dimopoulos, Maria P.
        affil: Program director of the radiation therapy program at Mount Sinai Center for Radiation Sciences Education at Stony Brook University in New York, New York
      sug:
        subj:
          Professional Practice
          Artificial Intelligence
          Radiotherapy Education
          Program Implementation
          Models, Educational
          Algorithms
          Curriculum
          Radiation Oncology
          Faculty
          Simulations
          Students, Radiologic Technology
          Research Ethics
      ab: This article focuses on the integration of artificial intelligence (AI) into radiation sciences education at the Mount Sinai Center for Radiation Sciences Education at Stony Brook University. It describes a multidimensional, practice-aligned approach embedding AI tools—including large language models (LLMs), virtual reality (VR), and simulation platforms like a virtual linear accelerator (LINAC)—into radiation therapy and medical dosimetry curricula to enhance clinical readiness, interprofessional collaboration, personalized learning, and ethical use. The program incorporates AI-supported treatment planning, SMART goal development aided by LLMs, immersive VR training for MR-LINAC environments, and AI-enhanced assessment aligned with certification standards, all while emphasizing academic integrity and professional accountability. The center also pursues faculty development, curriculum updates, and research literacy initiatives to prepare students for AI-enabled clinical practice and ongoing technological advances in radiation oncology.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        pictorial
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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