Artificial intelligence-based education assists medical students’ interpretation of hip fracture
Open accessAbstract
BACKGROUND: With recent transformations in medical education, the integration of technology to improve medical students' abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees' medical image learning. MATERIALS: We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy. RESULTS: The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group. CONCLUSION: The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.
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Several studies pointed to the infrastructure, financial resources, and investments necessary to implement AI-driven education methods, as the costs of servers, faculty training, tool development, and technical personnel could be substantial.
“Finally, several studies pointed to the infrastructure, financial resources, and investments necessary to implement AI-driven education methods [9, 50, 181], as the costs of servers, faculty training, tool development, and technical personnel could be substantial [182–184].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [184]
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Cites
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
Sample Size and Saturation in PhD Studies Using Qualitative Interviews
Mason, M. (2010) · Forum Qualitative Sozialforschung / Forum: Qualitative Social Research · doi:10.17169/fqs-11.3.1428 · Cited by 2,786 · see citing works
Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works
Current Applications and Future Impact of Machine Learning in Radiology
Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V., Brink, J. A., & Dreyer, K. J. (2018) · Radiology · doi:10.1148/radiol.2018171820 · Cited by 800 · see citing works
Chan, K. S., & Zary, N. (2019) · JMIR Medical Education · doi:10.2196/13930 · Cited by 765 · see citing works
Towards Explainable Artificial Intelligence
Samek, W., & Müller, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_1 · Cited by 650 · see citing works
Truong, H. M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.02.014 · Cited by 531 · see citing works
Artificial intelligence for analyzing orthopedic trauma radiographs
Olczak, J., Fahlberg, N., Maki, A., Razavian, A. S., Jilert, A., Stark, A., Sköldenberg, O. G., & Gordon, M. (2017) · Acta Orthopaedica · doi:10.1080/17453674.2017.1344459 · Cited by 482 · see citing works
Sapci, A. H., & Sapci, H. A. (2020) · JMIR Medical Education · doi:10.2196/19285 · Cited by 377 · see citing works
Machine learning and medical education
Kolachalama, V. B., & Garg, P. S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0061-1 · Cited by 320 · see citing works
Cheng, C., Ho, T., Lee, T.-Y., Chang, C.-C., Chou, C.-C., Chen, C.-C., Chung, I., & Liao, C. (2019) · European Radiology · doi:10.1007/s00330-019-06167-y · Cited by 311 · see citing works
Self-citationImplementing Machine Learning in Radiology Practice and Research
Kohli, M. D., Prevedello, L. M. S., Filice, R. W., & Geis, J. R. (2017) · American Journal of Roentgenology · doi:10.2214/ajr.16.17224 · Cited by 301 · see citing works
Reimagining Medical Education in the Age of AI
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.146 · Cited by 263 · see citing works
Translating Artificial Intelligence Into Clinical Care
Beam, A. L., & Kohane, I. S. (2016) · JAMA · doi:10.1001/jama.2016.17217 · Cited by 224 · see citing works
Artificial intelligence for precision education in radiology
Duong, M. T., Rauschecker, A. M., Rudie, J. D., Chen, P.-H., Cook, T. S., Bryan, R. N., & Mohan, S. (2019) · The British Journal of Radiology · doi:10.1259/bjr.20190389 · Cited by 205 · see citing works
What should medical students know about artificial intelligence in medicine?
Park, S. H., Do, K.-H., Kim, S., Park, J. H., & Lim, Y.-S. (2019) · Journal of Educational Evaluation for Health Professions · doi:10.3352/jeehp.2019.16.18 · Cited by 196 · see citing works
A Decade of Reports Calling for Change in Medical Education: What Do They Say?
Skochelak, S. E. (2010) · Academic Medicine · doi:10.1097/acm.0b013e3181f1323f · Cited by 157 · see citing works
“Flipping” the Introductory Clerkship in Radiology
Belfi, L. M., Bartolotta, R. J., Giambrone, A. E., Davi, C., & Min, R. J. (2015) · Academic Radiology · doi:10.1016/j.acra.2014.11.003 · Cited by 144 · see citing works
Bernard, J., Chang, T., Popescu, E., & Sabine Graf, A. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.021 · Cited by 141 · see citing works
Where medical education meets artificial intelligence: ‘Does technology care?’
van der Niet, A. G., & Bleakley, A. (2020) · Medical Education · doi:10.1111/medu.14131 · Cited by 128 · see citing works
Effects of Mobile Learning in Medical Education: A Counterfactual Evaluation
Briz-Ponce, L., Méndez, J. A. J., García‐Peñalvo, F. J., & Pereira, A. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0487-4 · Cited by 122 · see citing works
Metrics for Radiologists in the Era of Value-based Health Care Delivery
Sarwar, A., Boland, G. W., Monks, A., & Kruskal, J. B. (2015) · Radiographics · doi:10.1148/rg.2015140221 · Cited by 101 · see citing works
Precision Education Initiative: Moving Toward Personalized Education
Hart, S. A. (2016) · Mind Brain and Education · doi:10.1111/mbe.12109 · Cited by 93 · see citing works
Do Computers Teach Better? A Media Comparison Study for Case-based Teaching in Radiology
Maleck, M., Fischer, M. R., Kammer, B., Zeiler, C., Mangel, E., Schenk, F., & Pfeifer, K.-J. (2001) · Radiographics · doi:10.1148/radiographics.21.4.g01jl091025 · Cited by 90 · see citing works
Lam, C. Z., Nguyen, H. N., & Ferguson, E. C. (2016) · American Journal of Roentgenology · doi:10.2214/ajr.15.15020 · Cited by 49 · see citing works
Petra Welter, A., Deserno, T. M., Fischer, B., Günther, R. W., & Spreckelsen, C. (2011) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-11-68 · Cited by 32 · see citing works
Terashita, T., Tamura, N., Kisa, K., Kawabata, H., & Ogasawara, K. (2016) · BMC Medical Education · doi:10.1186/s12909-016-0753-7 · Cited by 30 · see citing works
Stone, J. R., Wilde, E. A., Taylor, B. A., Tate, D. F., Levin, H. S., Bigler, E. D., Scheibel, R. S., Newsome, M. R., Mayer, A. R., Abildskov, T. J., et al. (2016) · Brain Injury · doi:10.1080/02699052.2016.1222080 · Cited by 29 · see citing works
Video Coaching Improving Contemporary Technical and Nontechnical Ability in Laparoscopic Education
Liao, C., Ooyang, C.-H., Chen, C.-C., Liao, C.-A., Cheng, C., Hsieh, M., Hsieh, C., Tsai, C., Yeh, T., Yeh, C., et al. (2019) · Journal of surgical education · doi:10.1016/j.jsurg.2019.11.012 · Cited by 24 · see citing works
Self-citationQuality metrics currently used in academic radiology departments: results of the QUALMET survey
Walker, E. A., Petscavage-Thomas, J. M., Fotos, J. S., & Bruno, M. A. (2017) · British Journal of Radiology · doi:10.1259/bjr.20160827 · Cited by 23 · see citing works
Reeder and Felson's Gamuts in Radiology : Comprehensive Lists of Roentgen Differential Diagnosis
Reeder, M. M., Felson, B., Bradley, W. G., & Merritt, C. R. B. (2003) · Cited by 23 · see citing works
The Apprenticeship Model for Surgical Training Is Inferior
Lubowitz, J. H., Provencher, M. T., Brand, J. C., & Rossi, M. J. (2015) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2015.07.014 · Cited by 19 · see citing works
Attending Radiologist Variability and Its Effect on Radiology Resident Discrepancy Rates
Wildenberg, J. C., Chen, P., Scanlon, M. H., & Cook, T. S. (2017) · Academic Radiology · doi:10.1016/j.acra.2016.12.004 · Cited by 18 · see citing works
Pietrzak, B., Ward, A., Cheung, M. K., Schwendimann, B. A., Mollaoglu, G., Duong, M. T., Ulltveit-Moe, N., Allareddy, V., Dutton‐Regester, K., Zhang, J., et al. (2018) · Science · doi:10.1126/science.aau3877 · Cited by 17 · see citing works
What Is the Future of Electronic Learning in Radiology?
Flanders, A. E. (2007) · Radiographics · doi:10.1148/rg.272065192 · Cited by 15 · see citing works
Cook, T. S., Hernandez, J., Scanlon, M. H., Langlotz, C. P., & Li, C.-D. L. (2016) · Academic Radiology · doi:10.1016/j.acra.2016.03.008 · Cited by 11 · see citing works
Chen, P., Loehfelm, T. W., Kamer, A. P., Lemmon, A. B., Cook, T. S., & Kohli, M. D. (2016) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-016-9872-2 · Cited by 8 · see citing works
Strengthening the Radiology Learning Environment
Slanetz, P. J., Reede, D. L., Ruchman, R. B., Catanzano, T. M., Oliveira, A. M., Ortiz, D., Whorms, D. S., Carrodeguas, E., & Deitte, L. A. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.04.013 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · see citing works
Banerjee, M., Chiew, D., Patel, K. T., Johns, I., Chappell, D., Linton, N., Cole, G. D., Francis, D. P., Szram, J., Ross, J., et al. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02870-x · Cited by 168 · see citing works
AI in Medical Education: Global situation, effects and challenges
Zhang, W., Cai, M., Lee, H. J., Evans, R., Zhu, C., & Ming, C. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12009-8 · Cited by 148 · see citing works
The Impact of AI on Teaching and Learning in Higher Education Technology
Singh, S. V., & Hiran, K. K. (2022) · Journal of Higher Education Theory and Practice · doi:10.33423/jhetp.v22i13.5514 · Cited by 136 · see citing works
Digital transformation in engineering education: Exploring the potential of AI-assisted learning
Pham, T. T., Nguyen, T. B., Ha, S., & Ngoc, N. T. N. (2023) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8825 · Cited by 108 · see citing works
Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education
Hamilton, A. J. (2024) · Cureus · doi:10.7759/cureus.59747 · Cited by 106 · see citing works
Jung, T., Scanu, B., Brasier, C. M., Webber, J., Milenković, I. L., Corcobado, T., Tomšovský, M., Pánek, M., Bakonyi, J., Maia, C. M. B. D. F., et al. (2020) · Forests · doi:10.3390/f11010093 · Cited by 88 · see citing works
Ren, X., & Wu, M. L. (2025) · TechTrends · doi:10.1007/s11528-025-01055-3 · Cited by 136 · see citing works
Marengo, A., Pagano, A., Pange, J., & Soomro, K. A. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-11-2023-0218 · Cited by 91 · see citing works
Shaw, K., Henning, M. A., & Webster, C. S. (2025) · Medical Science Educator · doi:10.1007/s40670-025-02373-0 · Cited by 58 · see citing works
Akutay, S., Kaçmaz, H. Y., & Kahraman, H. (2024) · Nurse Education in Practice · doi:10.1016/j.nepr.2024.104142 · Cited by 55 · see citing works
Artificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62 · see citing works
Leimanis, A., & Palkova, K. (2021) · European Journal of Sustainable Development · doi:10.14207/ejsd.2021.v10n1p90 · Cited by 48 · see citing works
Applications of Artificial Intelligence in Medical Education: A Systematic Review
Hallquist, E., Gupta, I., Montalbano, M. J., & Loukas, M. (2025) · Cureus · doi:10.7759/cureus.79878 · Cited by 47 · see citing works
A Framework for Interpretability in Machine Learning for Medical Imaging
Wang, A. Q., Karaman, B. K., Kim, H., Rosenthal, J., Saluja, R., Young, S. I., & Sabuncu, M. R. (2024) · IEEE Access · doi:10.1109/access.2024.3387702 · Cited by 37 · see citing works
Roveta, A., Castello, L. M., Massarino, C., Francese, A., Ugo, F., & Maconi, A. (2025) · AI · doi:10.3390/ai6090227 · Cited by 42 · see citing works
Deep Learning for Detecting Supraspinatus Calcific Tendinopathy on Ultrasound Images
Chiu, P., Boudier‐Revéret, M., Chang, S., Wu, C., Chen, W., & Özçakar, L. (2022) · Journal of Medical Ultrasound · doi:10.4103/jmu.jmu_182_21 · Cited by 25 · see citing works
Exploring the integration of artificial intelligence in radiology education: A scoping review
Hui, M. L., Sacoransky, E., Chung, A., & Kwan, B. Y. (2024) · Current Problems in Diagnostic Radiology · doi:10.1067/j.cpradiol.2024.10.012 · Cited by 24 · see citing works
Beketov, V., Lebedeva, M., & Taranova, M. (2024) · Current Psychology · doi:10.1007/s12144-023-05471-7 · Cited by 21 · see citing works
Applications of deep learning method of artificial intelligence in education
Zhang, F., Wang, X., & Zhang, X. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12883-w · Cited by 21 · see citing works
Xia, J., Ge, Y., Shen, Z., & Najar, M. R. (2024) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v25i3.7680 · Cited by 26 · see citing works
Kim, T., Goh, T. S., Lee, J. S., Lee, J. H., Kim, H., & Jung, I. D. (2023) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-023-01215-w · Cited by 21 · see citing works
Peng, J., Zhang, H., Tu, X., Zhang, X., Wu, Q., Wang, Y., & Deng, X. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07770-y · Cited by 19 · see citing works
Bridging Motivation and AI in Education: An Activity Theory Perspective
Artemova, I. (2024) · Digital Education Review · doi:10.1344/der.2024.45.59-67 · Cited by 25 · see citing works
COVID-19 and Virtual Medical Student Education
Park, A., & Awan, O. A. R. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.04.011 · Cited by 16 · see citing works
Qin, H., Ding, Y., Ju, J., Qu, Z., & Peng, L. (2025) · Annals of Medicine · doi:10.1080/07853890.2025.2610079 · Cited by 16 · see citing works
Paré, G., Raymond, L., Pomey, M., Grégoire, G., Castonguay, A., & Ouimet, A. G. (2022) · Digital Health · doi:10.1177/20552076221114195 · Cited by 15 · see citing works
Domrös-Zoungrana, D., Rajaeean, N., Boie, S. D., Fröling, E., & Lenz, C. (2024) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205241284719 · Cited by 17 · see citing works
Li, J., Ouyang, J., Liu, J., Zhang, F., Wang, Z., Guo, X., Liu, M., & Taylor, D. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2190483 · Cited by 13 · see citing works
Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee
Glicksman, M., Wang, S., Yellapragada, S., Robinson, C. L., Orhurhu, V., & Emerick, T. D. (2024) · Pain Practice · doi:10.1111/papr.13428 · Cited by 12 · see citing works
Dadon, Z., Butnaru, A., Rosenmann, D., Alper-Suissa, L., Glikson, M., & Alpert, E. A. (2022) · AEM Education and Training · doi:10.1002/aet2.10738 · Cited by 13 · see citing works
Marengo, A., Pagano, A., Soomro, K. A., & Pange, J. (2023) · Preprints.org · doi:10.20944/preprints202311.0055.v1 · Cited by 13 · see citing works
A research roadmap for AI opportunities in student assessment for medical education
Rezaei-Zadeh, M., & Cerbin‐Koczorowska, M. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08078-7 · Cited by 14 · see citing works
O’Rourke, S., Xu, S., Carrero, S., Drebin, H. M., Felman, A., Ko, A., Misseldine, A., Mouchtaris, S., Musialowicz, B., Wong, T. T., et al. (2025) · Skeletal Radiology · doi:10.1007/s00256-025-04927-0 · Cited by 11 · see citing works
Zhang, W., Xiong, Y., Zhou, D., Liu, C., Gu, Y., & Huilin, Y. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2482590 · Cited by 11 · see citing works
Preparing Allergists to Practice in 2050 Using Artificial Intelligence
Khoury, P., Oppenheimer, J., Bunyavanich, S., Ciaccio, C. E., & Portnoy, J. M. (2025) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2025.09.012 · Cited by 10 · see citing works
Applications and Effects of EdTech in Medical Education
Hong, H., & Kim, Y. (2021) · Korean Medical Education Review · doi:10.17496/kmer.2021.23.3.160 · Cited by 9 · see citing works
Medical Education 4.0: A Neurology Perspective
Zafar, Z., Umair, M., Faheem, F., Bhatti, D., & Kalia, J. S. (2022) · Cureus · doi:10.7759/cureus.31668 · Cited by 9 · see citing works
Al-Saadawi, A., Tehranchi, S., Ahmed, S. S., & Nzeako, O. (2025) · Cureus · doi:10.7759/cureus.81671 · Cited by 8 · see citing works
Lai, N. M., Lim, Y. S., Thein, W. M., Bhargava, P., Thomas, P., & Ong, Q. C. (2026) · JMIR Medical Education · doi:10.2196/88933 · Cited by 10 · see citing works
Lin, S.-Y., Chiang, P., Chen, M., Lee, M.-Y., Lin, W., & Chen, Y. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103441 · Cited by 8 · see citing works
The Impact of Procedural Law on Artificial Intelligence by Improving the Healthcare Systems
Bozorgi, M. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-6684-8337-4.ch010 · Cited by 7 · see citing works
Alamer, A. (2022) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405618666220907111422 · Cited by 7 · see citing works
Radziievska, I., Trepet, G. S., Radzikhovska, N., Sukhostavets’, N. P., Yuryk, O., & Saienko, V. (2022) · Revista Amazonia Investiga · doi:10.34069/ai/2022.55.07.12 · Cited by 6 · see citing works
Ye, P., Li, S., Wang, Z., Tian, S., Luo, Y., Wu, Z., Zhuang, Y., Zhang, Y., Grzegorzek, M., & Hou, Z. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1146910 · Cited by 5 · see citing works
Dao, L., Harmouch, S. S., Chin, A. S.-L., Dao, T., Thian, Z., Chartrand‐Lefebvre, C., & Cohen, J. (2022) · medRxiv · doi:10.1101/2022.07.11.22277278 · Cited by 6 · see citing works
e-Learning in Radiological Image Interpretation for Medical Students: A Systematic Review
Lee, K., Tapia, K. A., Brennan, P., Suleiman, M. E., Jones, C. M., & Ekpo, E. U. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.06.055 · Cited by 4 · see citing works
Tee, Y., Liao, C.-A., Kuo, L., Hsu, C.-P., Wang, C., Fu, C., Liao, C., & Cheng, C. (2026) · World Journal of Emergency Surgery · doi:10.1186/s13017-026-00678-y · Cited by 4 · see citing works
Self-citationBokan, R. R., Malhotra, R., Vatsa, M., Bisht, K., Singla, M., & Choudhary, R. (2026) · Cureus · doi:10.7759/cureus.105503 · Cited by 4 · see citing works
Costa-Dookhan, K. A., Adirim, Z. L., Maslej, M. M., Donner, K., Rodak, T., Soklaridis, S., Sockalingam, S., & Thakur, A. (2025) · Academic Medicine · doi:10.1097/acm.0000000000005983 · Cited by 4 · see citing works
Ringeval, M., Raymond, L., Pomey, M.-P., & Paré, G. (2025) · Journal of Medical Internet Research · doi:10.2196/64804 · Cited by 4 · see citing works
Beyond the hype: evidence-based approaches to responsible AI integration in workplace learning
Sposato, M. (2025) · Education + Training · doi:10.1108/et-01-2025-0034 · Cited by 3 · see citing works
Verdone, A., Cardall, A., Siddiqui, F. A., Nashawaty, M., Rigau, D. N., Kwon, Y. J., Yousef, M., Patel, S., Kieturakis, A. J., Kim, E., et al. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.12.024 · Cited by 3 · see citing works
Artificial Intelligence in the Internal Medicine Clerkship: Results of a National Survey
Kumar, N. L., McQuade, C. N., Bonifacino, E., Alexandraki, I., Hufmeyer, K. K., Kisielewski, M., Lai, C. J., McBee, E. C., Patel, P., & Osman, N. Y. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10354-1 · Cited by 3 · see citing works
Artificial Intelligence in Social Sciences
Rosmayanti, V. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2612-2.ch012 · Cited by 3 · see citing works
Santos, L. F. F. M., Sánchez‐Tena, M. Á., Alvarez-Peregrina, C. C., Sánchez‐González, J., & Martínez-Pérez, C. (2025) · Technologies · doi:10.3390/technologies13020077 · Cited by 3 · see citing works
AI in Healthcare Education: A Systematic Review of Applications in Teaching and Learning
Bakthavatchaalam, V., & Sivasankar, K. (2024) · Approaches to global sustainability, markets, and governance · doi:10.1007/978-981-97-9555-0_13 · Cited by 2 · see citing works
Kondapaneni, N., Perona, P., & Aodha, O. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19806-9_24 · Cited by 1 · see citing works
Artificial intelligence and radiology – Where are we headed?
Rajak, A., & Thapa, N. B. (2022) · Journal of Kathmandu Medical College · doi:10.3126/jkmc.v11i4.50792 · Cited by 1 · see citing works
Framework Design of a Multi-Educational-Agent System for University Lecturers
Yang, K., & Niu, L. (2024) · International Conference on Intelligent Education and Intelligent Research (IEIR) · doi:10.1109/ieir62538.2024.10960068 · Cited by 1 · see citing works
Emerging Trends and Big Ideas for the Future of Artificial Intelligence (AI) in Education
Venkateswaran, P. S., Kamatchi, S. D., & Ravikumar, V. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1827-1.ch012 · Cited by 1 · see citing works
Samra, G. S., Ramoutar, V., Chen, K., Chaudhry, M. Q., Patel, H., Bird, T., & Rodwell, V. (2025) · The Clinical Teacher · doi:10.1111/tct.70139 · Cited by 1 · see citing works
Albaik, M., Al-Qahtani, S. A., Mantargi, M. J. S., Alghamdi, A., Sindi, I. A., Sheikh, R. A., Kamel, M. S., & Kurdi, L. A. F. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3255 · Cited by 1 · see citing works
Andreas, S., Pana, Z. D., Larentzakis, A. V., Kondylis, S., Maina, A. M., Ziogas, N. K., & Ntourakis, D. (2026) · Global Surgical Education - Journal of the Association for Surgical Education · doi:10.1007/s44186-025-00411-4 · Cited by 1 · see citing works
Zhao, L., Han, Q., Li, P., Dai, H., & Ju, X. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08947-9 · Cited by 1 · see citing works
Matua, G. A., Shanmugam, V., Jothilal, V. N., Auguskani, J. P. L., Arumugam, Y., Ravi, K., Jayapal, S. K., & Arulappan, J. (2026) · Journal of research in nursing · doi:10.1177/17449871261445369 · Cited by 3 · see citing works
Empowering Faculty Vitality and Mitigating Burnout Through Generative AI in Higher Education
Evans, S. Y. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1351-0.ch014 · Cited by 3 · see citing works
Caiza-Calle, R. M., & Espinoza, G. F. S. (2025) · MQRInvestigar · doi:10.56048/mqr20225.9.2.2025.e546
Davids, J., Lam, K., Nimer, A., Giannarou, S., & Ashrafian, H. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_30-1
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Kolostoumpis, G., Vasila-Vakhnina, O., & Vasilas, N. (2022) · Advances in medical education, research, and ethics (AMERE) book series · doi:10.4018/978-1-7998-8871-0.ch015
Davids, J., Lam, K., Nimer, A., Giannarou, S., & Ashrafian, H. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_30
Wu, J., Zheng, Q., Liu, J., Gong, T., & Jin, L. (2024) · International Symposium on Artificial Intelligence for Education (ISAIE) · doi:10.1145/3700297.3700355
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Kariya, P. (2024) · African Journal of Biomedical Research · doi:10.53555/ajbr.v27i4s.4959
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Khalid, M. N. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02555-5_35
Evaluating Generative AI as an Educational Tool for Radiology Resident Report DraftingPreprint
Verdone, A., Cardall, A., Siddiqui, F. A., Nashawaty, M., Rigau, D. N., Kwon, Y., Yousef, M., Patel, S. J., Kieturakis, A. J., Kim, E. S., et al. (2025) · medRxiv · doi:10.1101/2025.10.06.25337417
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Pedro Errázuriz, G. (2025) · Revista Chilena de Reumatología/Revista chilena de reumatología · doi:10.58450/rcr.v41i3.153
Lai, N. M., Lim, Y. S., Thein, W. M., Bhargava, P., Paraidathathu, T., & Ong, Q. C. (2025) · doi:10.2196/preprints.88933
Banerjee, M., Chiew, D., Patel, K. T., Johns, I., Chappell, D., Linton, N., Cole, G. D., Francis, D. P., Szram, J., Ross, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15178941
Safdar, A., Iftikhar, H., Arshad, H. A., Anwar, M., Khan, A. Q., & Kanwal, S. (2025) · VAWKUM Transactions on Computer Sciences · doi:10.21015/vtcs.v13i2.2278
Ringeval, M., Raymond, L., & Paré, G. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09432-z
Rincón, E. H. H., Bastidas, J. C. K., Acevedo, S. B., & Florez, N. C. R. (2026) · Journal of Investigative Medicine · doi:10.1177/10815589261460883
Reith, T. P., D’Alessandro, D. M., Modi, K. A., Sabawi, M., Zadeh, C. S., Sebaaly, M. G., Lai, L. M., Sato, T. S. P., Kao, S. C.-S., Satō, Y., et al. (2026) · Pediatric Radiology · doi:10.1007/s00247-026-06708-w
Rouzrokh, P., Rouzrokh, P., Khosravi, B., Shahkarami, F., Amini, M., Mathur, M., Erickson, B. J., & Garner, H. W. (2026) · Radiographics · doi:10.1148/rg.250198
Effectiveness of AI-Assisted Orthopedic Nursing Education: A Mixed-Methods Randomized ControlledPreprint
Yao, Z., Lin, G., Li, Y., He, S., Chen, Y., Pan, J., Dong, G., Ouyang, H., Li, Y., Qin, F., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10274926/v1
Use of artificial intelligence in education and training of radiology
Cevallos, M. B. M., Lam, C. B. M., López-Pino, M. J., Moreno, A. M. M., & López, D. F. V. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2026.1899739 · Cited by 1 · see citing works
Lauron, L. K. A. Y., Rayco, R. C. M., Laping, I. P., Rejollo, M. R. A., & Tagalog, R. J. (2026) · doi:10.68050/jams.2026.516
- Authors
- 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.
- Affiliations
- Chang Gung University (TW)
- National Yang Ming University (TW)
- Corresponding author
- Liao, Chien-Hung
- ORCID
- Venue
- Insights into Imaging
- Published
- 2020-11-23
- Volume
- 11
- Issue
- 1
- Pages
- 119-119
- ISSN
- 1869-4101 (ISSN-L)
- PMC
- PMC7683624
- OpenAlex
- W3109963111
- Cited by
- 87
- References
- 38
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 16 · 2023: 9 · 2022: 13 · 2021: 5 · 2020: 1
- FWCI
- 2.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Insights into Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Chang Gung Memorial Hospital, Linkou (CMRPGJ1121, CMRPG 3J631, CIRPG3H0021)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- artificial intelligence, hip fracture, medical image interpretation, diagnostic accuracy, medical students, radiology education
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