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Analysis of AI-Generated Radiography Responses Using a Closed-System LLM
Radiologic Technology
|April 23, 2026
Summary
Microsoft Copilot shows potential as a radiography study aid, but experts found its responses sometimes inaccurate or outdated. Critical evaluation is essential for safe use in radiologic science education.
Area of Science:
- Radiologic Science Education
- Artificial Intelligence in Healthcare
Background:
- Artificial intelligence (AI) tools are increasingly explored for educational support.
- Microsoft Copilot (GPT-4) offers potential assistance in radiography learning.
Purpose of the Study:
- To assess the accuracy and educational value of Microsoft Copilot's radiography responses.
- To identify strengths, limitations, and implications for radiologic science education.
Main Methods:
- Qualitative descriptive study evaluating Copilot's responses to 15 radiography exam questions.
- Seven subject matter experts assessed AI-generated content for accuracy and utility.
- Thematic analysis of expert feedback using a 6-phase framework.
Main Results:
- Experts noted Copilot's clarity and organizational strengths as a supplemental study aid.
- Concerns included incomplete content, outdated terminology, scope of practice issues, and lack of sourcing.
- AI responses sometimes lacked specificity and sufficient clinical context.
Conclusions:
- Copilot can offer structured support but requires critical appraisal due to potential inaccuracies.
- AI literacy skills are crucial for radiography students to evaluate AI outputs.
- Further research needed on AI platform comparison and safe integration into health professions education.
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