AI-Assisted Educational Interventions in Medical Imaging Education: A Systematic Review of Learning Outcomes, Diagnostic Competencies, and Implementation
DOI:
https://doi.org/10.68050/JAMS.2026.516Keywords:
Educational technology, Artificial intelligence, Medical imaging education, Radiology education, Diagnostic accuracy, Educational interventions, Generative artificial intelligence.Abstract
Background: Artificial intelligence (AI) is increasingly integrated into medical-imaging education to support image interpretation, personalised learning, feedback, simulation, and competency development. However, evidence regarding its educational effectiveness and implementation remains heterogeneous.
Objective: This systematic review evaluated AI-assisted educational interventions in medical-imaging education, focusing on learning outcomes, diagnostic competencies, and learner-reported and implementation outcomes.
Methods: Electronic databases and supplementary sources were searched for studies evaluating learner-facing AI-assisted educational interventions across medical-imaging disciplines. Eligible controlled, uncontrolled, single-group, and within-participant intervention studies were included. Findings were synthesised narratively because of substantial heterogeneity in learner populations, AI technologies, study designs, interventions, comparators, and outcomes. Objective effectiveness outcomes were considered separately from learner-reported and implementation outcomes.
Results: Seventeen unique studies met the eligibility criteria. Controlled studies generally reported favourable effects of AI-assisted approaches on image interpretation, diagnostic performance, personalised learning, and feedback. Uncontrolled studies also reported improvements in knowledge or diagnostic competency, although causal interpretation was limited by methodological limitations. Learner-reported and implementation findings were generally favourable, particularly for confidence, perceived knowledge, usefulness, and acceptability. Important concerns included hallucinations, reliability, technological overreliance, accountability, data security, and clinical fidelity. Meta-analysis was not undertaken because of substantial heterogeneity.
Conclusion: AI-assisted educational interventions show promise for improving selected learning and diagnostic outcomes in medical-imaging education. However, heterogeneous methods, small samples, uncontrolled designs, short follow-up, and reliance on self-reported outcomes limit certainty. AI should therefore complement, rather than replace, structured educator-led instruction.
References
1. Arif, W. M. (2024). Radiologic technology students’ perceptions on adoption of artificial intelligence technology in radiology. International Journal of General Medicine, 17, 3129–3136.
https://doi.org/10.2147/IJGM.S465944
2. Bala, W., Li, H., Moon, J., Trivedi, H., Gichoya, J., & Balthazar, P. (2025). Enhancing radiology training with GPT-4: Pilot analysis of automated feedback in trainee preliminary reports. Current Problems in Diagnostic Radiology, 54(2), 151–158. https://doi.org/10.1067/j.cpradiol.2024.08.003
3. Biswas, S. (2023). Artificial intelligence in radiology education. International Journal of Radiology & Radiation Therapy, 10(1), 1–3. https://doi.org/10.15406/ijrrt.2023.10.00344
4. Cacho, R. (2024). Integrating generative AI in university teaching and learning: A model for balanced guidelines. Online Learning, 28(3), 55–81. https://doi.org/10.24059/olj.v28i3.4508
5. Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., Welch, V., & Thomson, H. (2020). Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline. BMJ, 368, l6890.
https://doi.org/10.1136/bmj.l6890
6. Cayabyab, M. A. S., Rejollo, M. R. A., Rayco, R. C. M., Laping, I. P., Tan, J. A., Castañares, L. D. W., Castro, A. M. L., Melford, C. M. (2026). Impact of AI-Assisted Chest Radiography as a Triage Tool for Molecular Lab Testing in Rural Philippines: A Systematic Review of Diagnostic Yield and Health Equity. International Journal of Research and Innovation in Social Science, 10(6),
https://doi.org/10.47772/IJRISS.2026.100600430
7. Chapellier, P., Ferrari, J., Saliba, T., Jeltsch, P., Mohamed, M., Jankovski, S., Ilanjian, G., Epis, M., Pansini, V., Bragaglia, F., Agostinelli, A., Caringal, K., Lalov, L., Rotzinger, D. C., & Fahrni, G. (2026). Large language model-generated differential diagnoses in radiology education: Comparison with a standard casebook. Diagnostics, 16(13), 2009. https://doi.org/10.3390/diagnostics16132009
8. Chau, M., Arruzza, E., & Johnson, N. (2022). Simulation-based education for medical radiation students: A scoping review. Journal of Medical Radiation Sciences, 69(3), 367–381.
https://doi.org/10.1002/jmrs.572
9. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
10. Chen, Y., Sun, Z., Lin, W., Xv, Z., & Su, Q. (2025). Artificial intelligence in the training of radiology residents: A multicenter randomized controlled trial. Journal of Cancer Education, 40(2), 234–240.
https://doi.org/10.1007/s13187-024-02502-0
11. Cheng, C.-T., Chen, C.-C., Fu, C.-Y., Chaou, C.-H., Wu, Y.-T., Hsu, C.-P., Chang, C.-C., Chung, I.-F., Hsieh, C.-H., Hsieh, M.-J., & Liao, C.-H. (2020). Artificial intelligence-based education assists medical students’ interpretation of hip fracture. Insights into Imaging, 11, 119. https://doi.org/10.1186/s13244-020-00932-0
12. Co, S.J. (2025). Artificial intelligence in Philippine education: A narrative review of applications,perceptions, and challenges. International Research Journal of Science, Technology, Education, and Management, 5(2), 25-38. https://doi.org/10.5281/zenodo.15902950
13. Crotty, E., Singh, A., Neligan, N., Chamunyonga, C., & Edwards, C. (2024). Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development. Radiography, 30, 67–73. https://doi.org/10.1016/j.radi.2024.10.012
14. Doherty, G., McLaughlin, L., Hughes, C., McConnell, J., Bond, R., & McFadden, S. (2024). A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff. Radiography, 30(2), 474–482. https://doi.org/10.1016/j.radi.2023.12.019
15. Donkor, A., Boakye, E., Atuanor, P., & Wiafe, Y. A. (2025). Evaluation of a classroom-based medical imaging artificial intelligence educational intervention in Ghana: A pre-test/post-test study design. Radiography, 31(4), 102987. https://doi.org/10.1016/j.radi.2025.102987
16. du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630
17. Duong, M. T., Rauschecker, A. M., Rudie, J. D., Chen, P.-H., Cook, T. S., Bryan, R. N., & Mohan, S. (2019). Artificial intelligence for precision education in radiology. British Journal of Radiology, 92(1103), 20190389. https://doi.org/10.1259/bjr.20190389
18. Finkelstein, M., Ludwig, K., Kamath, A., Halton, K. P., & Mendelson, D. S. (2024). The impact of an artificial intelligence certificate program on radiology resident education. Academic Radiology, 31(11), 4709–4714. https://doi.org/10.1016/j.acra.2024.05.041
19. Garin, S. P., Zhang, V., Jeudy, J., Parekh, V. S., & Yi, P. H. (2023). Systematic review of radiology residency artificial intelligence curricula: Preparing future radiologists for the artificial intelligence era. Journal of the American College of Radiology, 20(6), 561–569.
https://doi.org/10.1016/j.jacr.2023.02.031
20. Gokkurt Yilmaz, B. N., Ozbey, F., & Yilmaz, B. E. (2025). Effect of artificial intelligence-assisted personalized feedback on radiographic diagnostic performance of dental students: A controlled study. BMC Medical Education, 25, 1403. https://doi.org/10.1186/s12909-025-07875-4
21. Hernández-Rodríguez, J., Rodríguez-Conde, M.-J., Santos-Sánchez, J.-Á., & Cabrero-Fraile, F.-J. (2023). Development and validation of an educational software based in artificial neural networks for training in radiology (JORCAD) through an interactive learning activity. Heliyon, 9(4), e14780. https://doi.org/10.1016/j.heliyon.2023.e14780
22. International Society of Radiographers and Radiological Technologists. (ISRRT, 2004, November). Guidelines for the education of entry-level professional practice in medical radiation sciences. https://www.isrrt.org/wp-content/uploads/2023/07/Document_6_Standards_of_Education.pdf
23. Keshavarz, P., Mohammadigoldar, Z., Bedayat, A., Raman, S. S., & Tai, R. (2026). Artificial intelligence education in radiology training: A systematic review of effectiveness, barriers, and future directions. Academic Radiology, 33(3), 695–706. https://doi.org/10.1016/j.acra.2025.10.049
24. Kim, S. H., Schramm, S., Wihl, J., Raffler, P., Tahedl, M., Canisius, J., Luiken, I., Endrös, L., Reischl, S., Marka, A., Walter, R., Schillmaier, M., Zimmer, C., Wiestler, B., & Hedderich, D. M. (2025). Boosting LLM-assisted diagnosis: 10-minute LLM tutorial elevates radiology residents’ performance in brain MRI interpretation. Neuroradiology, 67(8), 2069–2081. https://doi.org/10.1007/s00234-025-03664-4
25. Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420
26. Lai, N. M., Lim, Y. S., Win, M. T., Bhargava, P., Thomas, P., & Ong, Q. C. (2026). The effectiveness of artificial intelligence in undergraduate health professions education: Systematic review and meta-analysis of randomized controlled trials. JMIR Medical Education, 12, e88933.
27. Laupichler, M. C., Hadizadeh, D. R., Wintergerst, M. W. M., von der Emde, L., Paech, D., Dick, E. A., & Raupach, T. (2022). Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: A single group pre-and post-test study. BMC Medical Education, 22, Article 803. https://doi.org/10.1186/s12909-022-03866-x
28. Lee, S. E., Kim, H. J., Jung, H. K., Jung, J. H., Jeon, J.-H., Lee, J. H., Hong, H., Lee, E. J., Kim, D., & Kwak, J. Y. (2024). Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance. Frontiers in Endocrinology, 15, 1372397. https://doi.org/10.3389/fendo.2024.1372397
29. Létourneau, A., Deslandes Martineau, M., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. npj Science of Learning, 10, 29. https://doi.org/10.1038/s41539-025-00320-7
30. Lewis, S., Bhyat, F., Casmod, Y., Gani, A., Gumede, L., Hajat, A., Hazell, L., Kammies, C., Mahlaola, T. B., Mokoena, L., & Vermeulen, L. (2024). Medical imaging and radiation science students' use of artificial intelligence for learning and assessment. Radiography, 30(Suppl. 2), 60–66.
https://doi.org/10.1016/j.radi.2024.10.006
31. Luo, J., Zheng, C., Yin, J., & Teo, H. H. (2025). Design and assessment of AI-based learning tools in higher education: A systematic review. International Journal of Educational Technology in Higher Education, 22, 42. https://doi.org/10.1186/s41239-025-00540-2
32. Lyu, X., Dong, L., Fan, Z., Sun, Y., Zhang, X., Liu, N., & Wang, D. (2024). Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students. BMC Medical Education, 24, 740. https://doi.org/10.1186/s12909-024-05723-5
33. Masters, K., MacNeil, H., Benjamin, J., Carver, T., Nemethy, K., Valanci-Aroesty, S., Taylor, D. C. M., Thoma, B., & Thesen, T. (2025). Artificial intelligence in health professions education assessment: AMEE Guide No. 178. Medical Teacher, 47(9), 1410–1424.
https://doi.org/10.1080/0142159X.2024.2445037
34. Melford, C. M., O’Brien-Melford, M. A. P. (2026). Artificial Intelligence Applications in Clinical Laboratory Diagnostics: a Systematic Review of Diagnostic Accuracy, Workflow Efficiency, and Clinical Utility. International Journal of Research and Scientific Innovation (IJRSI), 13(4), https://doi.org/10.51244/IJRSI.2026.1304000242
35. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
https://doi.org/10.1136/bmj.n71
36. Salastekar, N. V., Maxfield, C., Hanna, T. N., Krupinski, E. A., Heitkamp, D. E., & Grimm, L. J. (2023). Artificial intelligence/machine learning education in radiology: Multi-institutional survey of radiology residents in the United States. Academic Radiology, 30(7), 1481–1487.
https://doi.org/10.1016/j.acra.2023.01.005
37. Suazo-Galdamés, I. C., & Chaple-Gil, A. M. (2025). AI-powered adaptive learning systems in higher education: A scoping review of implementation and impact on academic performance. Data and Metadata, 4, 981. https://doi.org/10.56294/dm2025981
38. Stogiannos, N., Skelton, E., Kumar, S., Ahmed, S., Amedu, C., Vince, C., Schiavottiello, M., O’Sullivan, C., & Malamateniou, C. (2025). Evaluation of a customised, AI-focused educational seminar delivered to final year undergraduate radiography students in the UK: A cross-sectional study. Radiography, 31(3), 102926. https://doi.org/10.1016/j.radi.2025.102926
39. Tabuchi, H., Engelmann, J., Maeda, F., Nishikawa, R., Nagasawa, T., Yamauchi, T., Tanabe, M., Akada, M., Kihara, K., Nakae, Y., Kiuchi, Y., & Bernabeu, M. O. (2024). Using artificial intelligence to improve human performance: Efficient retinal disease detection training with synthetic images. British Journal of Ophthalmology, 108(10), 1430–1435. https://doi.org/10.1136/bjo-2023-324923
40. UNESCO. (2023). Global education monitoring report 2023: Technology in education—A tool on whose terms? United Nations Educational, Scientific and Cultural Organization.
https://doi.org/10.54676/UZQV8501
41. United Nations Educational, Scientific and Cultural Organization. (UNESCO, 2025). Philippines: Artificial intelligence readiness assessment report.
https://unesdoc.unesco.org/ark:/48223/pf0000393860
42. Van Kooten, M. J., Tan, C. O., Hofmeijer, E. I. S., van Ooijen, P. M. A., Noordzij, W., Lamers, M. J., Kwee, T. C., Vliegenthart, R., & Yakar, D. (2024). A framework to integrate artificial intelligence training into radiology residency programs: Preparing the future radiologist. Insights into Imaging, 15, 15. https://doi.org/10.1186/s13244-023-01595-3
43. Verdone, A., Cardall, A., Siddiqui, F., Nashawaty, M., Rigau, D., Kwon, Y., Yousef, M., Patel, S., Kieturakis, A., Kim, E., Heacock, L., Reig, B., & Shen, Y. (2026). Evaluating generative artificial intelligence as an educational tool for radiology resident report drafting. Journal of the American College of Radiology, 23(5), 818–826. https://doi.org/10.1016/j.jacr.2025.12.024
44. Vergara, J. P. C. (2025). Navigating the generative artificial intelligence era: Charting the course for curricular reform in higher education in the Philippines. Second Congressional Commission on Education. https://edcom2.gov.ph/publications/navigating-the-generative-artificial-intelligence-era-charting-the-course-for-curricular-reform-in-higher-education-in-the-philippines/
45. Villarino, R. T. (2025). Artificial Intelligence (AI) integration in Rural Philippine Higher Education: Perspectives, challenges, and ethical considerations. IJERI: International Journal of Educational Research and Innovation, (23). https://doi.org/10.46661/ijeri.10909
46. Wang, D., Huai, B., Ma, X., Jin, B., Wang, Y., Chen, M., Sang, J., & Liu, R. (2024). Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching. BMC Medical Education, 24, Article 405.
https://doi.org/10.1186/s12909-024-05382-6
47. Wang, X., Huang, R., Sommer, M., Pei, B., Shidfar, P., Rehman, M. S., Ritzhaupt, A. D., & Martin, F. (2024). The efficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: A meta-analysis. Journal of Educational Computing Research, 62(6), 1568–1603.
https://doi.org/10.1177/07356331241240459
48. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39.
https://doi.org/10.1186/s41239-019-0171-0
49. Zhong, D., & Chow, S. K. K. (2025). Investigating the potential of generative AI clinical case-based simulations on radiography education: A pilot study. Journal of Imaging Informatics in Medicine, 39(2), 1848–1860. https://doi.org/10.1007/s10278-025-01601-8
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