Rare diseases pose significant diagnostic and therapeutic challenges, carrying a high disease burden, their management critically reflects a nation's public health resilience. Currently, China faces key challenges such as scarce treatments, fragmented services, and low drug accessibility in rare disease care, which urgently require systemic solutions. Digital-intelligent technology as a key breakthrough are expected to resolve the challenges in this field. Although its application in the field of rare diseases is gradually expanding, there is a lack of systematic compilation of studies to elucidate how to precisely enhance the precision, synergy and sustainability of diagnosis and treatment. The key challenges in rare disease care concentrate in four areas: inefficiency in prenatal screening, uneven distribution of medical resources, low efficiency in social organization collaboration, and ineffective information dissemination. The "4C" strategy, based on digital-intelligent technology, can address these issues: ①coordination, boost prenatal screening awareness and capacity via digital-intelligent platforms to strengthen prevention; ②cooperation, deepen collaboration within specialist networks, empowering institutions to enhance diagnostic capacity; ③co-creation, empower support organizations to optimize resources, efficiency; ④cognition, minimize information dissipation through efficient platforms, improving patient and family quality of life. This establishes an integrated digital-intelligent rare disease model encompassing "screening-diagnosis-treatment-care".

1.Cortés-Martín J, Sánchez-García JC, Rodríguez-Blanque R. Health care on rare diseases[J]. Int J Environ Res Public Health, 2022, 20(1): 395. DOI: 10.3390/ijerph20010395.

2.Ninomiya K, Okura M. Nationwide comprehensive epidemiological study of rare diseases in Japan using a health insurance claims database[J]. Orphanet J Rare Dis, 2022, 17(1): 140. DOI: 10.1186/s13023-022-02290-0.

3.Mizoguchi H, Yamanka T, Kano S. Research and drug development activities in rare diseases: differences between Japan and Europe regarding influence of prevalence[J]. Drug Discov Today, 2016, 21(10): 1681-1689. DOI: 10.1016/j.drudis.2016.06.014.

4.Ferreira CR. The burden of rare diseases[J]. Am J Med Genet A, 2019, 179(6): 885-892. DOI: 10.1002/ajmg.a.61124.

5.全国罕见病学术团体联席会议. 中国罕见病定义研究报告(2021) [R/OL]. (2021-09-11) [2025-02-01]. https://www.docin.com/p-2987644561.html

6.中央人民共和国中央人民政府. 关于公布第一批罕见病目录的通知[EB/OL]. (2018-05-11) [2025-03-30]. https://www.gov.cn/zhengce/zhengceku/2018-12/31/content_5435167.htm

7.中央人民共和国中央人民政府. 关于公布第二批罕见病目录的通知[EB/OL]. (2023-09-18) [2025-03-30]. https://www.gov.cn/zhengce/zhengceku/202309/content_6905273.htm

8.王秉. 何为数智: 数智概念的多重含义研究[J]. 情报杂志, 2023, 42(7): 71-76. [Wang B. What is data-intelligence: multiple meanings of data-intelligence[J]. Journal of Intelligence, 2023, 42(7): 71-76.] DOI: 10.3969/j.issn.1002-1965.2023.07.011.

9.Xie S, Zhao W, Deng G, et al. Utilizing ChatGPT as a scientific reasoning engine to differentiate conflicting evidence and summarize challenges in controversial clinical questions[J]. J Am Med Inform Assoc, 2024, 31(7): 1551-1560. DOI: 10.1093/jamia/ocae100.

10.Weng H, Chen J, Ou A, et al. Leveraging representation learning for the construction and application of a knowledge graph for traditional Chinese medicine: framework development study[J]. JMIR Med Inform, 2022, 10(9): e38414. DOI: 10.2196/38414.

11.Ferber D, Wölflein G, Wiest IC, et al. In-context learning enables multimodal large language models to classify cancer pathology images[J]. Nat Commun, 2024, 15(1): 10104. DOI: 10.1038/s41467-024-51465-9.

12.Jin Q, Chen F, Zhou Y, et al. Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine[J]. NPJ Digit Med, 2024, 7(1): 190. DOI: 10.1038/s41746-024-01185-7.

13.中国日报. 医联亮相2025腾讯云上海峰会, 共绘智慧医疗新图景[EB/OL]. (2025-3-20) [2025-3-30]. https://cn.chinadaily.com.cn/a/202503/20/WS67dbe1b6a310510f19eecdbc.html.

14.圆心科技. 圆心科技赋能多层次医疗保障体系建设, 助力健康中国[EB/OL]. (2024-06-13) [2025-3-30]. https://m.baidu.com/bh/m/detail/ar_9241467386774976192.

15.北京协和医院. 跻身国际前沿、助力分级诊疗|罕见病 AI大模型“协和·太初”正式进入临床应用[EB/OL]. (2025-02-18) [2025-03-30]. https://www.pumch.cn/detail/40027.html.

16.李朝喜, 温德惠, 刘伟亮, 等. SMART 3D-SMI在甲状腺TI-RADS 4类结节良恶性鉴别诊断中的应用[J]. 影像科学与光化学, 2022, 40(3): 510-514. [Li CX, Wen DH, Liu WL, et al. Application of SMART 3D-SMI in the differential diagnosis of benign and malignant in TI-RADS 4 thyroid nodules[J]. Imaging Science and Photochemistry, 2022, 40(3): 510-514.] DOI: 10.7517/issn.1674-0475.211204.

17.雷芳, 杜亮, 董敏, 等. 基于人工智能的临床决策支持系统早期临床评估的透明化报告[J]. 中国全科医学, 2024, 27(10): 1267-1270. [Lei F, Du L, Dong M, et al. Transparent reporting of the early-stage clinical evaluation of clinical decision support systems based on artificial intelligence[J]. Chinese General Practice, 2024, 27(10): 1267-1270.] DOI: 10.12114/j.issn.1007-9572.2023.0668.

18.Hamzyan Olia JB, Raman A, Hsu CY, et al. A comprehensive review of neurotransmitter modulation via artificial intelligence: a new frontier in personalized neurobiochemistry[J]. Comput Biol Med, 2025, 189: 109984. DOI: 10.1016/j.compbiomed.2025.109984.

19.Shen S, Qi W, Liu X, et al. From virtual to reality: innovative practices of digital twins in tumor therapy[J]. J Transl Med, 2025, 23(1): 348. DOI: 10.1186/s12967-025-06371-z.

20.Gao F, Ding J, Gai B, et al. Interpretable multimodal fusion model for bridged histology and genomics survival prediction in pan-cancer[J]. Adv Sci (Weinh), 2025, 12(17): e2407060. DOI: 10.1002/advs.202407060.

21.高美虹, 尚学群. 利用人工智能预测癌症的易感性、复发性和生存期[J]. 生物化学与生物物理进展, 2022, 49(9): 1687-1702.[Gao MH, Shang XQ. Artificial intelligence-based prediction for cancer susceptibility, recurrence and survival[J]. Progress In Biochemistry and Biophysics, 2022, 49(9): 1687-1702.] DOI: 10.16476/j.pibb.2021.0334.

22.曾志童, 王朝昕, 王慧, 等. 基于国内外最新指南的慢性病个体化、精细化健康管理服务分析及我国发展前景——以糖尿病为例[J]. 中国全科医学, 2021, 24(9): 1037-1044. [Zeng ZT, Wang ZX, Wang H, et al. Individualized and precision health management for diabetes: evidence from the latest guidelines and development prospects in China[J]. Chinese General Practice, 2021, 24(9): 1037-1044.] DOI: 10.12114/j.issn.1007-9572.2021.00.158.

23.北京大学第一医院数智医学研究中心. 重磅！“肾说(KidneyTalk)”大模型正式上线[EB/OL]. (2025-01-17) [2025-03-30]. https://www.pkufh.com/Html/News/Articles/58685.html.

24.韩晓光, 朱小龙, 姜宇桢, 等. 人工智能与机器人辅助医学发展研究[J]. 中国工程科学, 2023, 25(5): 43-54. [Han XG, Zhu XL, Jiang YZ, et al. Development strategies for artificial intelligence and robotics in medicine[J]. Strategic Study of CAE, 2023, 25(5): 43-54.] DOI: 10.15302/J-SSCAE-2023.07.031.

25.张小亮, 戴作雷, 曹凯迪,等. 急诊智能化信息系统构建 [J]. 医学信息学杂志, 2020, 41(10): 75-78. [Zhang XL, Dai ZL, Cao KD, et al. Building of the intelligent emergency information system[J]. Journal of Medical Informatics, 2020, 41(10): 75-78.] DOI: 10.3969/j.issn.1673-6036.2020.10.015.

26.曹萌, 余孙婕, 曾辉,等. 基于区块链的医疗数据分级访问控制与共享系统[J]. 计算机应用, 2023, 43(5): 1518-1526.[Cao M, Yu SJ, Zeng H, et al. Hierarchical access control and sharing system of medical data based on blockchain[J]. Journal of Computer Applications, 2023, 43(5): 1518-1526.] DOI: 10.11772/j.issn.1001-9081.2022050733.

27.新华智见. 国内首个 AI儿科医生正式“上岗”! 整合超300位知名儿科专家临床经验和专家们数十年的高质量病历数据[EB/OL]. ( 2025-02-16) [2025-03-30]. https://baijiahao.baidu.com/s?id=1824089088812849785&wfr=spider&for=pc.

28.Google Health. Google Health-Products, competitors, financials, employees, headquarters locations[EB/OL]. [2025-07-15]. https://www.cbinsights.com/company/google-research.

29.弓孟春, 郭艳英, 郑熙弘, 等. 中国罕见病分级诊疗体系建设中的信息化系统考量[J]. 罕见病研究, 2024, 3(4): 527-534. [Gong MC, Guo YY, Zheng XH, et, al. Informatics consideration on the hierarchical system of rare diseases clinical care in China[J]. Journal of Rare Diseases, 2024, 3(4): 527-534.] DOI: 10.12376/j.issn.2097-0501.2024.04.017.

30.张文, 闫晓婷, 许莹, 等. 罕见病研究数据采集和应用的现状及伦理学思考[J]. 中国医学伦理学, 2023, 36(10): 1132-1137, 1154. [Zhang W, Yan XT, Xu Y, et, al. Current status and ethical considerations of data collection and application in rare disease research[J]. Chinses Medical Ethics, 2023, 36(10): 1132-1137, 1154.] DOI: 10.12026/j.issn.1001-8565.2023.10.12.

31.郭健, 刘鹏, 荆志成, 等. 中国国家罕见病注册系统建设及应用[J]. 罕见病研究, 2022, 1(1): 7-12. [Guo J, Liu P, Jing ZC, et al. Construction and application of national rare diseases registry system of China[J]. Journal of Rare Diseases, 2022, 1(1): 7-12.] DOI: 10.12376/j.issn.2097-0501.2022.01.002.

32.赵星月, 姚家琦, 从竹, 等. 推进“儿童友好”由表及里 [EB/OL]. (2025-04-14) [2025-07-10]. https://www.thepaper.cn/newsDetail_forward_30641705.

33.齐旭, 刘晶, 宋婧. 人工智能大模型竞争日趋白热化[EB/OL]. (2023-02-28) [2025-07-10]. https://dsjj.guiyang.gov.cn/newsite/zgsg/jsqy/202307/t20230724_81235835.html.

34.Köhler S, Carmody L, Vasileveky N, et al. Expansion of the human phenotype ontology (HPO) knowledge base and resources[J]. Nucleic Acids Res, 2019, 47(D1): D1018-D1027. DOI: 10.1093/nar/gky1105.

35.Wang AY, Liu C, Yang JY, et al. Fine-tuning large language models for rare disease concept normalization[J]. J Am Med Inf Assoc, 2024, 31(9): 2076-2083. DOI: 10.1093/jamia/ocae133.

36.周奇, 李沁原, 刘雅莉, 等. 罕见病指南的制订: 现状、挑战与机遇[J]. 协和医学杂志,2023, 14(3): 621-628. [Zhou Q, Li QY, Liu YL, et al. The development of guidelines for rare diseases: past, present and future[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(3): 621-628.] DOI: 10.12290/xhyxzz.20220360.

37.Nguengang Wakap S, Lambert DM, Olry A, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database[J]. Eur J Hum Genet, 2020, 28(2): 165-173. DOI: 10.1038/s41431-019-0508-0.

38.顾学范, 韩连书, 余永国. 中国新生儿遗传代谢病筛查现状及展望[J]. 罕见病研究, 2022, 1(1): 13-19. [Gu XF, Han LS, Yu YG. Current status and prospects of screening for newborn hereditary metabolic disease[J]. Journal of Rare Diseases, 2022, 1(1): 13-19.] DOI: 10.12376/j.issn.2097-0501.2022.01.003.

39.刘慧玲, 赵恒伯, 谢志勤, 等. 2022年江西省罕见病治疗费用核算分析——基于SHA2011[J]. 卫生经济研究, 2025, 42(2): 46-50. [Liu HL, Zhao HB, Xie ZQ, et al. Analysis of rare disease treatment cost accounting in Jiangxi province in 2022[J]. Health Economics Research, 2025, 42(2): 46-50.] DOI: 10.14055/j.cnki.33-1056/f.2025.02.015.

40.丁若溪, 张蕾, 赵艺皓, 等. 罕见病流行现状——一个极弱势人口的健康危机[J]. 人口与发展, 2018, 24(1): 72-84. [Ding RX, Zhang L, Zhao YH, et al. Prevalence of rare diseases: the health crisis for an extremely vulnerable population[J]. Population & Development, 2018, 24(1): 72-84.] https://ccj.pku.edu.cn/article/info?aid=217550766.

41.刘鑫, 唐彦, 张波, 等. 超说明书用药在罕见病治疗领域的应用现状[J]. 国际药学研究杂志, 2019, 46(9): 685-690. [Liu X, Tang Y, Zhang B, et al. Off-label drug use in the treatment of rare diseases: the current situation[J]. Journal of International Pharmaceutical Research, 2019, 46(9): 685-690.] DOI: 10.13220/j.cnki.jipr.2019.09.007.

42.杨贇滢, 袁萍, 李梅, 等. 117例儿童脊髓性肌萎缩症自然病史分析[J]. 中国当代儿科杂志, 2021, 23(10): 1038-1043. [Yang YY, Yuan P, Li M, et al. Natural history of spinal muscular atrophy in children: an analysis of 117 cases[J]. Chinese Journal of Contemporary Pediatrics, 2021, 23(10): 1038-1043.] DOI: 10.7499/j.issn.1008-8830.2106025.

43.中华人民共和国中央人民政府. 国家卫生健康委办公厅关于建立全国罕见病诊疗协作网的通知[EB/OL]. (2019-02-12) [2025-03-30]. https://www.gov.cn/zhengce/zhengceku/2019- 10/08/content_5436962.htm.

44.韩优莉. 医保支付方式由后付制向预付制改革对供方医疗服务行为影响的机制和发展路径[J]. 中国卫生政策研究, 2021, 14(3): 21-27. [Han YL. The mechanism and pathway of the health insurance payment reform from retrospective to prospective mode on the medical provider's behavior[J]. Chinese Journal of Health Policy, 2021,14(3): 21-27.] DOI: 10.3969/j.issn.1674-2982. 2021.03.004.

45.梁土坤. 罕见病群体社会工作服务: 需求、困境及对策 [J]. 社会工作与管理, 2016, 16(4): 13-22. [Liang TK. Social work services for patients with rare diseases: real needs, main difficulties and countermeasures[J]. Journal of Guangdong University of Technology (Social Sciences Edition), 2016, 16(4): 13-22.] DOI: 10.3969/j.issn.1671-623X.2016.04.002.

46.吴健, 孙曼曼, 张平, 等. “小马拉大车”: “罕见病”社会组织如何拉动政府、市场与社会救助力量——以北京市M脊髓性肌萎缩症关爱中心为例[J]. 社会保障研究, 2024, (5): 93-103. [Wu J, Sun MM, Zhang P, et al. "A pony pulls the heavy cart": how can rare disease social organizations obtain assistance resources from government,market and social sectors—a case study of Beijing M spinal muscular atrophy care center[J]. Social Security Studies, 2024, (5): 93-103.] https://kns.cnki.net/kcms2/article/abstract?v=TajfHsrud9_WTOnS-reuI07K2eJC8r03QA3eBRX7SeqawxLQZNxZhJ2J3d5_iH5IOuBqEixccRv7q6qF6j4U8mUBFvJQYDwugqIFE9vgfPUVyANkXbTEGJZIcxRmRw3tyEsDayj_-QtUqV90k7We8Ju_H8_3NrfkxY8b1zgohip6zG5SOtD-eA==&uniplatform=NZKPT&language=CHS.

47.熊贵彬, 刘丹. 罕见病社会救助网络组织的现状及作用——基于罕见病关爱中心调研[J]. 社会福利（理论版）, 2014, (11): 22-25. [Xiong GB, Liu D. Status and role of social assistance network organizations for rare diseases: based on a survey of rare disease care centers[J]. China Welfare, 2014, (11): 22-25.] https://www.cnki.com.cn/Article/CJFDTotal-SFLL201411006.htm.

48.李丹阳, 詹文英. 中国罕见病非营利组织发展的特点和困境[J]. 大家健康（学术版）, 2013, 7(18): 1, 4. [Li DY, Zhan WY. Characteristics and dilemmas of rare disease non-profit organizations in China[J]. For All Health, 2013, 7(18): 1, 4.] https://www.cnki.com.cn/Article/CJFDTotal-JKXS201318001.htm.

49.财富在线. 为爱呐“罕” 恒昌公益成立6载累计救助64名肠道重疾患儿[EB/OL]. (2025-02-28) [2025-03-30]. https://baijiahao.baidu.com/s?id=1825272515536136023&wfr=spider&for=pc.

50.澎湃新闻. 2025罕见病行业趋势报告: 诊疗和保障仍存困难, 多元力量参与[EB/OL]. [2025-03-04) [2025-03-30]. https://www.thepaper.cn/newsDetail_forward_30299081.

51.李艳颜, 唐海燕, 张家怡, 等. 母胎医学框架下的罕见病信息化精细化专科管理[J]. 中国产前诊断杂志（电子版）, 2024, 16(2): 45-49, 53. [Li YY, Tang HY, Zhang JY, et al. The development an information-based fine specialty of rare diseases under the framework of maternal-fetal medicine management[J]. Chinese Journal of Prenatal Diagnosis (Electronic Version), 2024, 16(2): 45-49, 53.] DOI: 10.13470/j.cnki.cjpd.2024.02.008.

52.李涵韬, 齐向东. 大语言模型结合数字人技术合成短视频在医学科普中的效果评价[J]. 组织工程与重建外科, 2024, 20(6): 643-647. [Li HT, Qi XD. Evaluation of the effectiveness of short videos synthesized by large language models combined with human video generation in medical science popularization[J]. Journal of Tissue Engineering and Reconstructive Surgery, 2024, 20(6): 643-647.] DOI: 10.3969/j.issn.1673-0364.2024.06.007.

53.王硕, 阎妍, 李正风. 生成式人工智能赋能科学普及：技术机遇、伦理风险与应对策略[J]. 科普研究, 2024, 19(4): 5-13, 22. [Wang S, Yan Y, Li ZF. Empowering science popularization through generative artificial intelligence: technological opportunities, ethical risks, and response strategies[J]. Science Popularization, 2024, 19(4): 5-13, 22.] DOI: 10.19293/j.cnki. 1673-8357.2024.04.001.

54.杨晰, 黄曼妮, 安菊生, 等. 生成式人工智能大模型应用于宫颈癌防治科普工作的分析[J]. 肿瘤学杂志, 2024, 30(9): 774-779. [Yang X, Huang MN, An JS, et al. Generative artificial intelligence models in public education on prevention of cervical cancer[J]. Journal of Chinese Oncology, 2024, 30(9): 774-779.] DOI: 10.11735/j.issn.1671-170X.2024.09.B010.

55.杨韵洁, 水科斌, 史颖悟, 等. 基于“互联网+”的高危儿管理平台的设计与应用[J]. 中国医疗设备, 2023, 38(12): 101-105, 112. [Yang YJ, Shui KB, Shi YW, et al. Design and application of management platform for high-risk infant based on "internet +"[J]. China Medical Devices, 2023, 38(12): 101-105, 112.] DOI: 10.3969/j.issn.1674-1633.2023.12.019.

56.苏雅洁, 蒋海丽, 阿仙, 等. 南疆地区新生儿家属对新生儿疾病筛查认知情况调查[J]. 中国妇幼健康研究, 2016, 27(5): 634-636. [Su YJ, Jiang HL, A X, et al. Cognition of parents on neonal disease screening knowledge in Southern Xinjiang[J]. Chinese Journal of Woman and Child Health Research, 2016, 27(5): 634-636.] DOI: 10.3969/j.issn.1673-5293.2016.05.029.

57.吴行伟, 刘馨宇, 龙恩武, 等. 机器学习在临床药物治疗中的研究进展[J]. 中国全科医学, 2022, 25(2): 254-258. [Wu XW, Liu XY, Long EW, et al. Research progress ofmachine learning in clinical drug therapy[J]. Chinese General Practice, 2022, 25(2): 254-258.] DOI: 10.12114/j.issn.1007-9572.2021.01.309.

58.董诚, 林立, 金海, 等. 医疗健康大数据: 应用实例与系统分析[J]. 大数据, 2015, 1(2): 78-89. [Dong C, Lin L, Jin H, et al. Big data in healthcare: applications and system analytics[J]. Big Data Research, 2015, 1(2): 78-89.] DOI: 10.11959/j.issn.2096- 0271.2015021.

59.周庆国, 陈奉贤, 李妍. 大语言模型驱动的临床决策支持：挑战与实践路径[J]. 兰州大学学报（医学版）, 2025, 51(4): 1-7. [Zhou QG, Chen FX, Li Y. Large language models for clinical decision support: challenges and implementation pathways[J]. Journal of Lanzhou University (Medical Sciences), 2025, 51(4): 1-7.] DOI: 10.13885/j.issn.1000-2812.2025.04.001.

60.施呈昊, 屠馨怡, 史佳伟, 等. 大语言模型临床实践应用范围综述[J]. 医学信息学杂志, 2024, 45(9): 19-26. [Shi CH, Tu XY, Shi JW, et al. A scoping review of the application of large language models in clinical practice[J]. Journal of Medical Intelligence, 2024, 45(9): 19-26.] DOI: 10.3969/j.issn.1673-6036.2024. 09.003.

61.马武仁, 弓孟春, 戴辉, 等. 以ChatGPT为代表的大语言模型在临床医学中的应用综述[J]. 医学信息学杂志, 2023, 44(7): 9-17. [Ma WR, Gong MC, Dai H, et al. A comprehensive review of the applications of large language models in clinical medicine with ChatGPT as a representative[J]. Journal of Medical Informatics, 2023, 44(7): 9-17.] DOI: 10.3969/j.issn.1673-6036.2023.07.002.

62.中国罕见病联盟, 北京罕见病诊疗与保障学会, 医学基因组委员会, 等. 促进基因检测技术在中国罕见病诊疗研究中的应用——医学基因组委员会的成立及工作展望[J]. 罕见病研究, 2024, 3(3): 275-279. [China Alliance for Rare Diseases, Beijing Society of Rare Disease Clinical Care and Accessibility, Medical Genome Committee, et al. To promote the application of genetic testing technology in the diagnosis andtreatment of rare diseases in China-establishment and work prospect of Medical Genome Committee[J]. Journal of Rare Diseases, 2024, 3(3): 275-279.] DOI: 10.12376/j.issn.2097-0501.2024.03.001.

63.孙艳华, 刘畅, 王子航, 等. 基于特征选择和特征表示的垂直联邦知识迁移算法[J/OL]. 北京工业大学学报, 1-10. [2025-07-16]. https://link.cnki.net/urlid/11.2286.T.20250529.1343.002.

64.赵玉英, 朱雯华, 戴毅. 青少年成人脊髓性肌萎缩症高危人群快速识别方法的探索与展望[J]. 罕见病研究, 2024, 3(3): 288-294. [Zhang YY, Zhu WH, Dai Y. To identify high-risk adolescent and adult spinal muscular atrophy populations: exploration of methods and perspectives[J]. Journal of Rare Diseases, 2024, 3(3): 288-294.] DOI: 10.12376/j.issn.2097-0501.2024.03.003.

65.卜禾, 段文杰, 查皓钰. 人工智能与社会工作协作的演进逻辑与赋能路径[J]. 华东理工大学学报（社会科学版）, 2024, 39(6): 56-66. [Bu H, Duan WJ, Zha HY. The evolutionary logic and empowerment path of collaboration between artificial intelligence and social work[J]. Journal of East China University of Science and Technology (Social Science Edition), 2024, 39(6): 56-66.] DOI: 10.3969/j.issn.1008-7672.2024.06.006.

66.廖其能. 职业情境下社会工作服务学习何以可能？——以粤北乡镇社工站示范建设专题课程实施为例[J]. 社会工作, 2024, (4): 132-149, 164-166. [Liao QN. How is service learning of social work possible in a professional context?: taking the implementation of special subject course on demonstration construction of social work stations in rural towns in northern guangdong as an example[J]. Journal of Social Work, 2024, (4): 132-149, 164-166.] https://kns.cnki.net/kcms2/article/abstract?v=TajfHsrud98OiivTcvGhzUEeeqsWqMwEoI2Te6jKOyr_bVbeeXHwxLUpCCtJ-XsTXit3YPrRRKNkKo2Mi-6VxPg7Ng4BmzrSquIZ-g39NeuxPTnQqiwQJtCbHYHAo3mCFxEaPkhgghXd2ejtTVq-GAtRUPN-X5L607qYRbz8k0VlE6tbsW27HA==&uniplatform=NZKPT&language=CHS.

67.何龙韬. AI+安宁疗护社会工作线上服务的干预设计研究[J]. 华东理工大学学报（社会科学版）, 2024, 39(6): 27-44, 66. [He LT. An intervention design study of AI+ online hospice care social work service[J]. Journal of East China University of Science and Technology (Social Science Edition) , 2024, 39(6): 27-44, 66.] DOI: 10.3969/j.issn.1008-7672.2024.06.004.

68.张瑞凯, 王玉佳. 人工智能技术应用于社会工作情感劳动的优势与风险[J]. 中国社会工作, 2023, (25): 28-30. [Zhang KR, Wang YJ. The application of artificial intelligence technology to emotional labor in social work advantages and risks[J]. China Social Work,  2023, (25): 28-30.] DOI: 10.3969/j.issn.1674-3857. 2023.25.017.

69.陈天怡. 社区社会工作数智化赋能研究[D]. 杭州: 杭州师范大学, 2022. DOI: 10.27076/d.cnki.ghzsc.2022.000732.