Academic Journal
Application Effectiveness Analysis of Artificial Intelligence-assisted Teaching in Standardized Training for Medical Imaging Residents.
| Τίτλος: | Application Effectiveness Analysis of Artificial Intelligence-assisted Teaching in Standardized Training for Medical Imaging Residents. |
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| Συγγραφείς: | Zhang L; Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, PR China (L.Z., Q.C., B.Z., J.F., X.M., S.Z.)., Chen Q; Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, PR China (L.Z., Q.C., B.Z., J.F., X.M., S.Z.)., Zhang B; Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, PR China (L.Z., Q.C., B.Z., J.F., X.M., S.Z.)., Fang J; Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, PR China (L.Z., Q.C., B.Z., J.F., X.M., S.Z.)., Mo X; Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, PR China (L.Z., Q.C., B.Z., J.F., X.M., S.Z.)., Zhang S; Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, PR China (L.Z., Q.C., B.Z., J.F., X.M., S.Z.). Electronic address: shui7515@126.com. |
| Πηγή: | Academic radiology [Acad Radiol] 2026 Sep; Vol. 33 (9), pp. 3619-3627. Date of Electronic Publication: 2026 Jul 11. |
| Τύπος έκδοσης: | Journal Article; Randomized Controlled Trial |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Reston Va : Association Of University Radiologists Original Publication: Reston, VA : Association of University Radiologists, c1994- |
| Ιατρικοί όροι (MeSH): | Radiology*/education , Computer-Assisted Instruction*/methods , Artificial Intelligence* , Internship and Residency*, Humans ; Clinical Competence ; Male ; Female ; Tomography, X-Ray Computed ; Educational Measurement ; Teaching ; Adult |
| Περίληψη: | Rationale and Objectives: This study evaluated the efficacy of a combined artificial intelligence (AI)-assisted and traditional teaching model in enhancing radiology residents' diagnostic competency for pulmonary nodules on chest Combined Teaching (CT). Materials and Methods: In this randomized controlled trial, 36 s-year residents were allocated to three groups: Traditional Teaching (TT), AI-Assisted Teaching (AI-AT), and CT. All received standardized theory instruction. TT underwent 2 h of mentored film-reading. AI-AT completed 2 h of AI platform training. CT received 1 h of AI training followed by 1 h of expert-led review of challenging cases. Assessments included theoretical and practical exams, diagnostic process metrics, and a 1-month follow-up survey on confidence, cognitive load, and AI perceptions. Results: The CT group significantly outperformed both the TT and AI-AT groups in post-test theoretical (CT: 91.8 ± 3.4 vs. TT: 82.6 ± 3.2, P < 0.001; vs. AI-AT: 86.2 ± 2.9, P = 0.003) and practical scores (CT: 93.5 ± 2.7 vs. TT: 81.9 ± 3.0, P < 0.001; vs. AI-AT: 86.8 ± 3.0, P < 0.001). Analysis of diagnostic process metrics revealed the CT group engaged in more deliberate analysis (longer reading time, P = 0.01) and demonstrated superior completeness in describing key imaging features (P < 0.001). At the 1-month follow-up, the CT group maintained higher diagnostic confidence (P = 0.002) and exhibited a more balanced perception of AI as a collaborative tool. Conclusion: This exploratory randomized controlled trial demonstrates that the integration of AI-AT with traditional expert-led sessions is significantly more effective than either method alone in improving radiology residents' diagnostic performance and clinical reasoning for pulmonary nodules. This combined model fosters deeper cognitive engagement and sustainable learning outcomes, providing a framework for competency-based education in the AI era. (Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The AI software used in this study was for research/educational purposes only, and the authors have no financial or non-financial relationships with its developer. |
| Σχόλια: | Erratum in: Acad Radiol. 2026 Oct 15:S1076-6332(26)00821-4. doi: 10.1016/j.acra.2026.09.062.. (PMID: 42881008) |
| Contributed Indexing: | Keywords: Artificial intelligence; Pulmonary nodule; Radiology education; Resident training; Teaching model |
| Entry Date(s): | Date Created: 20260711 Date Completed: 20260820 Latest Revision: 20261015 |
| Update Code: | 20261016 |
| DOI: | 10.1016/j.acra.2026.06.022 |
| PMID: | 42436060 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1878-4046 |
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| DOI: | 10.1016/j.acra.2026.06.022 |