Analysis of AI-Generated Radiography Responses Using a Closed-System LLM.
Purpose To evaluate the accuracy and educational utility of Microsoft Copilot's (GPT-4, July 2025, closed-system version) responses to radiography questions through expert assessment, with a focus on strengths, limitations, and implications for radiologic science education. Methods This qualitative...
| Published in: | Radiologic Technology Vol. 97; no. 5; pp. 310 - 319 |
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| Format: | research tables/charts Journal Article |
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American Society of Radiologic Technologists
May/Jun2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195527040&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195527040 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00338397 39J jtl: Radiologic Technology issn: 00338397 maglogo: N pubinfo: dt: May/Jun2026 vid: 97 iid: 5 pid: 7052 pub: American Society of Radiologic Technologists place: Alburquerque, New Mexico artinfo: ui: 195527040 195527040 195527040 195527040 ppf: 310 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Analysis of AI-Generated Radiography Responses Using a Closed-System LLM. aug: au: Clark, Kevin R. affil: Associate professor and associate director with the School of Health Professions at The University of Texas MD Anderson Cancer Center in Houston sug: subj: Natural Language Processing Utilization Radiography Education Technology, Radiologic Computer-Assisted Instruction Reproducibility of Results Human Male Female Qualitative Studies Descriptive Research Thematic Analysis Scope of Practice Professional Role Practice Guidelines Communication Teaching Materials Radiologic Technologists Psychosocial Factors Computer Literacy Curriculum Artificial Intelligence, Generative Education, Radiologic Technology Male Female ab: Purpose To evaluate the accuracy and educational utility of Microsoft Copilot's (GPT-4, July 2025, closed-system version) responses to radiography questions through expert assessment, with a focus on strengths, limitations, and implications for radiologic science education. Methods This qualitative descriptive study evaluated Copilot's responses to 15 open-ended radiography questions derived from the American Registry of Radiologic Technologists Radiography Examination Content Specifications. Seven subject matter experts with extensive clinical and teaching experience independently reviewed the artificial intelligence (AI)--generated responses for accuracy and educational utility. Feedback was collected using Microsoft Forms and analyzed inductively following a 6-phase thematic analysis framework. Results Thematic analysis revealed 6 overarching themes: accuracy and completeness of content, scope of practice and role clarification, outdated terminology and standards, formatting and presentation strengths, utility as a learning aid, and need for specificity and context. Experts praised the clarity, structure, and organization of responses and noted their potential as supplemental study aids. However, concerns were raised about incomplete or superficial content, attributions outside a radiologic technologist's scope of practice, outdated terminology and standards (including shielding and grid use), lack of specificity, and insufficient clinical context. Discussion Findings suggested that although Copilot might provide structured and accessible support for radiography learners, its limitations could result in outdated or inaccurate practices if used without a critical lens. The closed-system design further constrained educational utility by preventing transparent sourcing. For radiography education, these results highlighted the importance of embedding critical AI literacy skills into curricula so that students learn to evaluate, verify, and contextualize AI-generated outputs. Conclusion Copilot demonstrated potential as a supplemental learning aid in radiography education, but outdated terminology, technical inaccuracies, and lack of sourcing constrained its reliability. Future research should compare multiple AI platforms, assess student learning outcomes, and explore strategies for embedding AI literacy and institutional safeguards to support safe, effective integration into health professions education. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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