Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship
Open accessAbstract
BACKGROUND: Artificial intelligence (AI) is an increasingly popular tool in medical investigations. However, AI's potential of aiding medical teaching has not been explored. This study aimed to evaluate the effectiveness of AI-tutoring problem-based-learning (PBL) in ophthalmology clerkship and to assess the student evaluations of this module. METHODS: Thirty-eight Grade-two students in ophthalmology clerkship at Sun Yat-Sen University were randomly assigned to two groups. In Group A, students learned congenital cataracts through an AI-tutoring PBL module by exploring and operating an AI diagnosis platform. In Group B, students learned congenital cataracts through traditional lecture given with the same faculty. The improvement in student performance was evaluated by comparing the pre- and post-lecture scores of a specific designed test using paired-T tests. Student evaluations of AI-tutoring PBL were measured by a 17-item questionnaire. RESULTS: The post-lecture scores were significantly higher than the pre-lecture scores in both groups (Group A: P<0.0001, Group B: P<0.0001). The improvement of group A in the part of sign and diagnosis test (Part I) was more significant than that of group B (P=0.016). However, there was no difference in the improvement in the part of treatment plan test (Part II) between two groups (P=0.556). Overall, all respondents were satisfied and agreed that AI-tutoring PBL was helpful, effective, motive and beneficial to help develop critical and creative thinking. CONCLUSIONS: The application of AI-tutoring PBL into ophthalmology clerkship improved students' performance and satisfaction. AI-tutoring PBL teaching showed advantage in promoting students' understanding of signs of diseases. The instructors play an indispensable role in AI-tutoring PBL curriculum.
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
Active learning increases student performance in science, engineering, and mathematics
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1319030111 · Cited by 9,787 · see citing works
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
Self-citationEhteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
Self-citationTing, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402 · see citing works
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works
The psychological basis of problem-based learning
Norman, G. R., & Schmidt, H. G. (1992) · Academic Medicine · doi:10.1097/00001888-199209000-00002 · Cited by 1,472 · see citing works
Does problem-based learning work? A meta-analysis of evaluative research
Vernon, D. T., & Blake, R. L. (1993) · Academic Medicine · doi:10.1097/00001888-199307000-00015 · Cited by 1,456 · see citing works
Increased Structure and Active Learning Reduce the Achievement Gap in Introductory Biology
Haak, D. C., HilleRisLambers, J., Pitre, E., & Freeman, S. (2011) · Science · doi:10.1126/science.1204820 · Cited by 959 · see citing works
Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer
Golden, J. A. (2017) · JAMA · doi:10.1001/jama.2017.14580 · Cited by 297 · see citing works
Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., & Kawashima, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179790 · Cited by 259 · see citing works
Lin, H., Li, R., Liu, Z., Chen, J., Yang, Y., Chen, H., Lin, Z., Lai, W., Long, E., Wu, X., et al. (2019) · EClinicalMedicine · doi:10.1016/j.eclinm.2019.03.001 · Cited by 199 · see citing works
Self-citationProblem-based learning in pre-clinical medical education: 22 years of outcome research
Hartling, L., Spooner, C., Tjosvold, L., & Oswald, A. (2010) · Medical Teacher · doi:10.3109/01421590903200789 · Cited by 191 · see citing works
Bate, E., Hommes, J. E., Duvivier, R. J., & Taylor, D. (2013) · Medical Teacher · doi:10.3109/0142159x.2014.848269 · Cited by 154 · see citing works
The psychological basis of problem-based learning: A review of the evidence
Norman, G. T., & Schmidt, H. (1992) · RePub (Erasmus University Rotterdam) · Cited by 149 · see citing works
Problem-based learning in comparison with lecture-based learning among medical students.
Faisal, R., Bahadur, S., & Shinwari, L. (2016) · PubMed · Cited by 79 · see citing works
Wang, Q., Li, H., & Pang, W. (2016) · Medical Education Online · doi:10.3402/meo.v21.31973 · Cited by 39 · see citing works
Modified Team-Based Learning in an Ophthalmology Clerkship in China
Huang, Z., Li, M., Zhou, Y., Ao, Y., Wei, X., Jia, Y., Yang, Y., Cai, Y. L., Xu, C., Yang, Y., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154250 · Cited by 36 · see citing works
Self-citationSurgical clerkship or medical clerkship first: Does it make a difference?
Adelsheimer, A., Berman, R. S., Pachter, H. L., & Hochberg, M. S. (2017) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2017.08.054 · Cited by 8 · see citing works
Artificial intelligence can augment global pathology initiatives – Authors' reply
Wilson, M. L., Sayed, S., Horton, S., & Fleming, K. A. (2018) · The Lancet · doi:10.1016/s0140-6736(18)32170-6 · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 23 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Application of Artificial Intelligence in Medicine: An Overview
Liu, P.-R., Lu, L., Zhang, J.-Y., Huo, T.-T., Liu, S.-X., & Ye, Z.-W. (2021) · Current Medical Science · doi:10.1007/s11596-021-2474-3 · Cited by 258 · see citing works
Artificial Intelligence Revolutionizing the Field of Medical Education
Narayanan, S., Ramakrishnan, R., Durairaj, E., & Das, A. (2023) · Cureus · doi:10.7759/cureus.49604 · Cited by 164 · see citing works
Ruiz-Viruel, S., Rivas, E. S., & Ruiz‐Palmero, J. (2025) · Education Sciences · doi:10.3390/educsci15020150 · Cited by 70 · see citing works
Wei, H., Dai, Y., Yuan, K., Li, K. Y., Hung, K. F., Hu, E. M., Lee, A. H. C., Chang, J. W. W., Zhang, C., & Li, X. (2025) · International Dental Journal · doi:10.1016/j.identj.2025.100858 · Cited by 58 · see citing works
Qian, X., Jingying, H., Xian, S., Zhao, Y., Wu, L., Baorui, C., Guo, W., Yefeng, Z., Zhang, Q., Chunyan, C., et al. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1025271 · Cited by 37 · see citing works
Chen, S., & Bai, W. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2025.1576465 · Cited by 37 · see citing works
Fang, Z. H., Xu, Z., He, X., & Han, W. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1053079 · Cited by 31 · see citing works
Artificial intelligence in corneal diseases: A narrative review
Nguyen, T. N., Ong, J., Masalkhi, M., Waisberg, E., Zaman, N., Sarker, P., Aman, S., Lin, H., Luo, M., Ambrósio, R., et al. (2024) · Contact Lens and Anterior Eye · doi:10.1016/j.clae.2024.102284 · Cited by 33 · see citing works
Self-citationArtificial Intelligence developments in medical education: a conceptual and practical framework
Masters, K. (2020) · MedEdPublish · doi:10.15694/mep.2020.000239.1 · Cited by 25 · see citing works
Digital Education in Ophthalmology
Al-Khaled, T., Acaba-Berrocal, L., Cole, E. D., Ting, D. S. W., Chiang, M. F., & Chan, R. V. P. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000484 · Cited by 28 · see citing works
Ren, S., Li, Y., Pu, L., & Feng, Y. (2023) · Worldviews on Evidence-Based Nursing · doi:10.1111/wvn.12663 · Cited by 26 · see citing works
Guo, S., Halim, H. B. A., & Saad, M. R. B. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-00801-0 · Cited by 23 · see citing works
Zheng, R., Xu, H., Wang, M., & Lu, J. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3488086 · Cited by 19 · see citing works
Keleş, H. (2022) · Kırıkkale Üniversitesi Tıp Fakültesi Dergisi · doi:10.24938/kutfd.1214512 · Cited by 19 · see citing works
Kasuga, W., Maro, W., & Pangani, I. N. (2022) · Journal of Turkish Science Education · doi:10.36681/tused.2022.154 · Cited by 18 · see citing works
Artificial intelligence in clinical education in ophthalmology: a systematic review
Yuan, L., Kang, D., Dong, X., Liu, L., Grzybowski, A. E., & Jin, K. (2025) · Visual Neuroscience · doi:10.48130/vns-0025-0025 · Cited by 17 · see citing works
Pattathil, N., Zhao, J., Sam-Oyerinde, O. A., & Felfeli, T. (2023) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100757 · Cited by 19 · see citing works
Ong, J., Hariprasad, S. M., & Chhablani, J. K. (2023) · Ophthalmic surgery, lasers & imaging retina · doi:10.3928/23258160-20230926-01 · Cited by 14 · see citing works
Kim, H.-S., & Kim, G. (2024) · Journal of Dental Sciences · doi:10.1016/j.jds.2024.08.020 · Cited by 13 · 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
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Hou, J., An, F., Qin, H., Zhang, L., Wang, J., Zhang, C., & Fan, D. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07852-x · Cited by 12 · see citing works
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Zafar, Z., Umair, M., Faheem, F., Bhatti, D., & Kalia, J. S. (2022) · Cureus · doi:10.7759/cureus.31668 · Cited by 9 · see citing works
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Niazai, H., & Monib, W. K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1519-5.ch004 · Cited by 7 · see citing works
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Kwon, Y. S., Dohopolski, M. J., Morgan, H., Garant, A., Sher, D. J., Rahimi, A. S., Sanford, N. N., Vo, D. T., Albuquerque, K., Kumar, K. A., et al. (2023) · Practical Radiation Oncology · doi:10.1016/j.prro.2022.09.003 · Cited by 6 · 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
Najafi, A., Babaei, S., Sadoughi, M.-M., Kalantarion, M., & Sadatmoosavi, A. (2025) · Journal of Ophthalmic and Vision Research · doi:10.18502/jovr.v20.17029 · Cited by 3 · see citing works
Dagher, T., Haydon, R. C., Wolf, J. M., & Strelzow, J. (2025) · Current Problems in Surgery · doi:10.1016/j.cpsurg.2025.101943 · Cited by 3 · see citing works
Salman, S. M. A., Taheer, T. B., Memon, S. S., Iqbal, Z., Yahya, U., & Khan, U. (2023) · Pakistan Journal of Medical and Health Sciences · doi:10.53350/pjmhs2023173742 · Cited by 3 · see citing works
TAY, Y. N., NEO, N. W. S., & SIAH, C. J. R. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107322 · Cited by 4 · see citing works
Alrashid, F. F., Khalifah, E. M., Alshammari, K. F., Alenazi, F., Alqahtani, A. S., Alshmmri, M. A., Alsolami, A. A., Alruwaili, S. H., Fathuldeen, A. A., Alshammari, A. O., et al. (2022) · Pakistan Journal of Medical and Health Sciences · doi:10.53350/pjmhs22162583 · Cited by 2 · see citing works
Semeniuk, R., Holovnia, Y., Huda, O., & Савастру, Н. (2024) · Futurity Education · doi:10.57125/fed.2024.03.25.02 · Cited by 10 · see citing works
Panni, U. Y., Donald, C. C., Blatnik, J. A., Williams, M., Yu, J., & Wise, P. E. (2025) · Journal of surgical education · doi:10.1016/j.jsurg.2025.103736 · Cited by 1 · see citing works
Perlmutter, J. W., & Joseph, M. (2025) · Education and human development · doi:10.5772/intechopen.1014025 · Cited by 2 · see citing works
Peng, Y., Ma, M., Yang, Y., Wu, J., & Peng, Y. (2026) · Anatomical Sciences Education · doi:10.1002/ase.70297 · Cited by 1 · see citing works
Artificial intelligence in cataract: What’s new?
Bhattacharjee, K., Rehman, O., Venkatraman, V., & Bhattacharjee, H. (2022) · Global Journal of Cataract Surgery and Research in Ophthalmology · doi:10.25259/gjcsro_4_2022 · Cited by 1 · see citing works
Naqvi, S. S. Q., Mahrukh, M., Zaidi, Y. F., Akram, B., Orakzai, G. S., & Islam, A. (2023) · Pakistan Journal of Medical and Health Sciences · doi:10.53350/pjmhs2023172799
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Rao, J. V. (2024) · Telangana journal of IMA. · doi:10.4103/tjima.tjima_18_24
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Shen, I. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82915-4_5
Yang, B. X., Zhou, F., Bai, N., Zhou, S., Luo, C., Wáng, Q., Wong, A. K. C., & Lin, F. (2025) · Frontiers of digital education. · doi:10.1007/s44366-025-0046-y · Cited by 2 · see citing works
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Podkovalihina, O., & Shcherbyna, D. S. (2025) · Наука і техніка сьогодні · doi:10.52058/2786-6025-2025-5(46)-1273-1287
Hanlon, C. D., Goldberg, H. R., & Cooper, S. (2025) · Medical Science Educator · doi:10.1007/s40670-025-02505-6
Nasri, N., Jamaludin, K. A., & Nasri, N. M. (2025) · Medical Science Educator · doi:10.1007/s40670-025-02585-4
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Golahmadi, A. K., Rad, A. A., & Athanasiou, T. (2026) · Biomedical visualization · doi:10.1007/978-3-032-06520-9_6
Kako, T., Kato, D., Iguchi, T., Shiyu, Q., Ando, M., KOSEKI, S., SHIBAHARA, H., MOTOI, H., Isaka, R., Ikeda, N., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-37300-9
The Application of Artificial Intelligence in Cataract Diagnosis and Treatment
王, 彦. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1641616
105Intelligent Learning: Generative AI in Higher Education and Authentic Learning
Shen, I. (2025) · De Gruyter eBooks · doi:10.1515/9783112206393-006
Kalhori, Z., Bajoulvand, R., Zahedian, M., Malgard, S., Aalaa, M., & Asadzandi, S. (2026) · Advances in Medical Education and Practice · doi:10.2147/amep.s592844
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Kurt, M. (2026) · Arşiv Kaynak Tarama Dergisi · doi:10.17827/aktd.1681629
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Pelletier, J., Arango-Ibañez, J. P., Taylor, R. A., Smith, M. E., Gottlieb, M. A., Jones, D. D., Colletti, J. E., Rego, A., & Long, B. (2026) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2026.09.006
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Butt, A. B., Leong, R., Balas, M., & Popovic, M. M. (2026) · Seminars in Ophthalmology · doi:10.1080/08820538.2026.2725232
- Authors
- Wu, D., Xiang, Y., Wu, X., Yu, T., Huang, X., Zou, Y., Liu, Z., & Lin, H.
- Affiliations
- Sun Yat-sen University (CN)
- Corresponding author
- Liu, Zhenzhen
- Venue
- Annals of Translational Medicine
- Publisher
- AME Publishing Company
- Published
- 2020-06
- Volume
- 8
- Issue
- 11
- Pages
- 700-700
- ISSN
- 2305-5839 (ISSN-L)
- 2305-5847
- PMC
- PMC7327320
- OpenAlex
- W2997244174
- Cited by
- 51
- References
- 23
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 5 · 2023: 7 · 2022: 8 · 2021: 2 · 2020: 2
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Annals of Translational Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81873675)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Problem and Project Based Learning
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- problem-based learning, congenital cataract, medical education, student satisfaction, treatment planning, critical and creative thinking
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