Applications and challenges of artificial intelligence in postgraduate medical education

doi:10.3760/cma.j.cn115259-20240715-00736

Abstract

Abstract: The integration of Artificial Intelligence (AI) technology is progressively reshaping the medical education. In recent years, represented by diagnostic assistance AI and generative AI, AI technology has been increasingly and extensively applied in various aspects of postgraduate medical education. This review introduces the progress of AI applications in postgraduate medical education, including assistance in teaching implementation, self-directed learning, and assessment and feedback. The review demonstrates the ability and potential of AI to enhance teaching efficiency and quality, promote precise and personalized clinical competence development, and facilitate the transition of physicians from traditional learning to a new model of human-computer collaboration. The review also discusses the challenges generated by AI technology, such as ethical issues, data security, educational equity, technology acceptance, and the cultivation of “medicine + AI” interdisciplinary professionals.

Key words: Artificial intelligence, Generative AI, Postgraduate medical education, Residency training, Application, Challenge

CLC Number:

Xie Wenjia, Wang Zhengyang. Applications and challenges of artificial intelligence in postgraduate medical education[J]. Chinese Journal of Medical Education, 2025, 45(3): 187-193.

References

[1] 方晏红, 张扬, 李航, 等. 毕业后医学教育的现状和思考[J]. 中国继续医学教育, 2020,12(14):85-87. DOI: 10.3969/j.issn.1674-9308.2020.14.032.
[2] 阮恒超, 樊立洁, 耿晓北, 等. 住院医师规范化培训基地评估结果的分析与思考[J]. 中华医学教育杂志, 2021,41(6):563-566. DOI: 10.3760/cma.j.cn115259-20210113-00064.
[3] Xu Y, Jiang Z, Ting D, et al. Medical education and physician training in the era of artificial intelligence[J]. Singapore Med J, 2024,65(3):159-166. DOI: 10.4103/singaporemedj.SMJ-2023-203.
[4] Lancet T. AI in medicine: creating a safe and equitable future[J]. Lancet, 2023,402(10401):503. DOI: 10.1016/S0140-6736(23)01668-9.
[5] Masters K. Artificial intelligence in medical education[J]. Med Teach, 2019,41(9):976-980. DOI: 10.1080/0142159X.2019.1595557.
[6] Cooper A, Rodman A. AI and medical education — a 21st-century pandora′s box[J]. N Engl J Med, 2023,389(5):385-387. DOI: 10.1056/NEJMp2304993.
[7] Klar R, Bayer U. Computer-assisted teaching and learning in medicine[J]. Int J Biomed Comput, 1990,26(1-2):7-27. DOI: 10.1016/0020-7101(90)90016-n.
[8] Pei L, Ak M, Tahon N, et al. A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network[J]. Sci Rep, 2022,12(1):10826. DOI: 10.1038/s41598-022-14983-4.
[9] Suk HI, Lee SW, Shen D, et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis[J]. Neuroimage, 2014,101:569-582. DOI: 10.1016/j.neuroimage.2014.06.077.
[10] Wang S, Li C, Wang R, et al. Annotation-efficient deep learning for automatic medical image segmentation[J]. Nat Commun, 2021,12(1):5915. DOI: 10.1038/s41467-021-26216-9.
[11] Suk HI, Lee SW, Shen D, et al. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis[J]. Brain Struct Funct, 2015,220(2):841-859. DOI: 10.1007/s00429-013-0687-3.
[12] Feng Y, Li J, Zhang X. Research on segmentation of brain tumor in MRI image based on convolutional neural network[J]. Biomed Res Int, 2022,2022:7911801. DOI: 10.1155/2022/7911801.
[13] Shen J, Zhang C, Jiang B, et al. Artificial intelligence versus clinicians in disease diagnosis: systematic review[J]. JMIR Med Inform, 2019,7(3):e10010. DOI: 10.2196/10010.
[14] Wu J, Yuan Z, Fang Z, et al. A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification[J]. Sci Rep, 2023,13(1):9003. DOI: 10.1038/s41598-023-36024-4.
[15] Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017,542(7639):115-118. DOI: 10.1038/nature21056.
[16] Pandey PU, Ballios BG, Christakis PG, et al. Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs[J]. Br J Ophthalmol, 2024,108(3):417-423. DOI: 10.1136/bjo-2022-322183.
[17] Chaddad A, Peng J, Xu J, et al. Survey of explainable AI techniques in healthcare[J]. Sensors (Basel), 2023,23(2):634. DOI: 10.3390/s23020634.
[18] Altintas L, Sahiner M. Transforming medical education: the impact of innovations in technology and medical devices[J]. Expert Rev Med Devices, 2024,21(9):797-809. DOI: 10.1080/17434440.2024.2400153.
[19] Sato Y, Takegami Y, Asamoto T, et al. Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study[J]. BMC Musculoskelet Disord, 2021,22(1):407. DOI: 10.1186/s12891-021-04260-2.
[20] Fang Z, Xu Z, He X, et al. Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program[J]. Front Cell Dev Biol, 2022,10:1053079. DOI: 10.3389/fcell.2022.1053079.
[21] Burke OM, Gwillim EC. Integrating artificial intelligence-based mentorship tools in dermatology[J]. Acad Med, 2024,99(6):e4. DOI: 10.1097/ACM.0000000000005705.
[22] Meetschen M, Salhofer L, Beck N, et al. AI-assisted X-ray fracture detection in residency training: evaluation in pediatric and adult trauma patients[J]. Diagnostics (Basel), 2024,14(6):596. DOI: 10.3390/diagnostics14060596.
[23] Aldeman N, de Sá Urtiga Aita KM, Machado VP, et al. Smartpath(k): a platform for teaching glomerulopathies using machine learning[J]. BMC Med Educ, 2021,21(1):248. DOI: 10.1186/s12909-021-02680-1.
[24] Tabuchi H, Engelmann J, Maeda F, et al. Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images[J]. Br J Ophthalmol, 2024,108(10):1430-1435. DOI: 10.1136/bjo-2023-324923.
[25] Retamero JA, Gulturk E, Bozkurt A, et al. Artificial intelligence helps pathologists increase diagnostic accuracy and efficiency in the detection of breast cancer lymph node metastases[J]. Am J Surg Pathol, 2024,48(7):846-854. DOI: 10.1097/PAS.0000000000002248.
[26] Muntean GA, Groza A, Marginean A, et al. Artificial intelligence for personalised ophthalmology residency training[J]. J Clin Med, 2023,12(5):1825. DOI: 10.3390/jcm12051825.
[27] Ricotta DN, Richards JB, Atkins KM, et al. Self-directed learning in medical education: training for a lifetime of discovery[J]. Teach Learn Med, 2022,34(5):530-540. DOI: 10.1080/10401334.2021.1938074.
[28] Desai SV, Burk-Rafel J, Lomis KD, et al. Precision education: the future of lifelong learning in medicine[J]. Acad Med, 2024,99(4S Suppl 1):S14-S20. DOI: 10.1097/ACM.0000000000005601.
[29] Boscardin CK, Gin B, Golde PB, et al. ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity[J]. Acad Med, 2024,99(1):22-27. DOI: 10.1097/ACM.0000000000005439.
[30] Alhur A. Redefining healthcare with artificial intelligence (AI): the contributions of ChatGPT, Gemini, and Co-pilot[J]. Cureus, 2024,16(4):e57795. DOI: 10.7759/cureus.57795.
[31] Laohawetwanit T, Apornvirat S, Kantasiripitak C. ChatGPT as a teaching tool: preparing pathology residents for board examination with AI-generated digestive system pathology tests[J]. Am J Clin Pathol, 2024,162(5):471-479. DOI: 10.1093/ajcp/aqae062.
[32] Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine[J]. N Engl J Med, 2023,388(13):1233-1239. DOI: 10.1056/NEJMsr2214184.
[33] Gordon M, Daniel M, Ajiboye A, et al. A scoping review of artificial intelligence in medical education: BEME Guide No. 84[J]. Med Teach, 2024,46(4):446-470. DOI: 10.1080/0142159X.2024.2314198.
[34] 梁菊, 李瑛. 模拟医学教育:医学教学发展的必然趋势[J].肾脏病与透析肾移植杂志, 2015,24(3):266-269.
[35] Gendia A. Cloud based AI-driven video analytics (CAVs) in laparoscopic surgery: a step closer to a virtual portfolio[J]. Cureus, 2022,14(9):e29087. DOI: 10.7759/cureus.29087.
[36] Hisey R, Camire D, Erb J, et al. System for central venous catheterization training using computer vision-based workflow feedback[J]. IEEE Trans Biomed Eng, 2022,69(5):1630-1638. DOI: 10.1109/TBME.2021.3124422.
[37] Mirchi N, Bissonnette V, Yilmaz R, et al. The virtual operative assistant: an explainable artificial intelligence tool for simulation-based training in surgery and medicine[J]. PLoS One, 2020,15(2):e229596. DOI: 10.1371/journal.pone.0229596.
[38] Baloul MS, Yeh VJ, Mukhtar F, et al. Video commentary & machine learning: tell me what you see, I tell you who you are[J]. J Surg Educ, 2022,79(6):e263-e272. DOI: 10.1016/j.jsurg.2020.09.022.
[39] Siyar S, Azarnoush H, Rashidi S, et al. Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task[J]. Med Biol Eng Comput, 2020,58(6):1357-1367. DOI: 10.1007/s11517-020-02155-3.
[40] Knudsen JE, Ghaffar U, Ma R, et al. Clinical applications of artificial intelligence in robotic surgery[J]. J Robot Surg, 2024,18(1):102. DOI: 10.1007/s11701-024-01867-0.
[41] Okuda Y, Bryson EO, DeMaria S Jr, et al. The utility of simulation in medical education: what is the evidence?[J]. Mt Sinai J Med, 2009,76(4):330-343. DOI: 10.1002/msj.20127.
[42] McGaghie WC, Issenberg SB, Petrusa ER, et al. A critical review of simulation-based medical education research: 2003-2009[J]. Med Educ, 2010,44(1):50-63. DOI: 10.1111/j.1365-2923.2009.03547.x.
[43] Muthusami A, Mohsina S, Sureshkumar S, et al. Efficacy and feasibility of objective structured clinical examination in the internal assessment for surgery postgraduates[J]. J Surg Educ, 2017,74(3):398-405. DOI: 10.1016/j.jsurg.2016.11.004.
[44] Ginsburg S, van der Vleuten C, Eva KW. The hidden value of narrative comments for assessment: a quantitative reliability analysis of qualitative data[J]. Acad Med, 2017,92(11):1617-1621. DOI: 10.1097/ACM.0000000000001669.
[45] Yilmaz Y, Jurado NA, Ariaeinejad A, et al. Harnessing natural language processing to support decisions around workplace-based assessment: machine learning study of competency-based medical education[J]. JMIR Med Educ, 2022,8(2):e30537. DOI: 10.2196/30537.
[46] Wood EA, Ange BL, Miller DD. Are we ready to integrate artificial intelligence literacy into medical school curriculum: students and faculty survey[J]. J Med Educ Curric Dev, 2021,8:1967551794. DOI: 10.1177/23821205211024078.
[47] Civaner MM, Uncu Y, Bulut F, et al. Artificial intelligence in medical education: a cross-sectional needs assessment[J]. BMC Med Educ, 2022,22(1):772. DOI: 10.1186/s12909-022-03852-3.
[48] Banerjee M, Chiew D, Patel KT, et al. The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers[J]. BMC Med Educ, 2021,21(1):429. DOI: 10.1186/s12909-021-02870-x.
[49] Naamati-Schneider L. Enhancing AI competence in health management: students′ experiences with ChatGPT as a learning tool[J]. BMC Med Educ, 2024,24(1):598. DOI: 10.1186/s12909-024-05595-9.
[50] Hswen Y, Voelker R. New AI tools must have health equity in their DNA[J]. JAMA, 2023,330(17):1604-1607. DOI: 10.1001/jama.2023.19293.
[51] Recai Y, Alexander W, Nykan M, et al. Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation[J]. npj Digital Medicine, 2022,5(1):54. DOI: 10.1038/s41746-022-00596-8.