Exploration and effect evaluation of a clinical teaching model in respiratory medicine based on the "LungSmart" smart healthcare system_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LI Yuhang ¹ , WU Junqi ² , CHEN Yi ² ,  HE Yayi ¹ ,  CHEN Chang ²

1. Department of Thoracic Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, P. R. China;
2. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, P. R. China;

Corresponding?author：

HE Yayi, Email: yayi.he@#edu.cn; CHEN Chang, Email: chenthoracic@163.com

Keywords：

Artificial intelligence; smart healthcare; medical education; "LungSmart" system; respiratory medicine

DOI：

10.7507/1007-4848.202602002

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To explore the teaching model of the "LungSmart" smart healthcare system in clinical pulmonology teaching and its effectiveness in enhancing the clinical reasoning skills of postgraduate students. Methods A single-center, single-group pretest-posttest educational intervention study was conducted among 30 postgraduate students who participated in respiratory medicine-related teaching activities and enrolled in the "LungSmart" smart healthcare course at Shanghai Pulmonary Hospital from 2024 to 2025 academic year. The course was structured around three core components, namely an AI case repository, dynamic simulation, and immediate feedback, and was delivered over 16 weeks with a total of 64 class hours. Teaching effectiveness was assessed using pretest and posttest clinical reasoning ability scores, while students’ acceptance of the course was evaluated using a 5-point Likert questionnaire. Results All 30 students completed the teaching activities and were included in the final analysis. The pretest score was (78.83±6.25) points, and the posttest score increased to (93.50±4.18) points, with a mean improvement of (14.67±7.06) points (95%CI, 12.03 to 17.30), indicating a statistically significant improvement after the intervention (t=11.37, P<0.001). A total of 30 valid questionnaires were collected at the end of the course, with a response rate of 100.0%. The overall satisfaction score was (4.67±0.15) points, and the mean scores for content satisfaction, practical value, interest stimulation, and professional competence were (4.72±0.23) points, (4.53±0.35) points, (4.72±0.39) points, and (4.75±0.43) points, respectively. Conclusion These findings suggest that the clinical teaching model based on the "LungSmart" smart healthcare system is feasible and well accepted, and may help improve postgraduate students’ clinical reasoning ability.

Citation： LI Yuhang, WU Junqi, CHEN Yi, HE Yayi, CHEN Chang. Exploration and effect evaluation of a clinical teaching model in respiratory medicine based on the "LungSmart" smart healthcare system. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2026, 33(8): 1203-1211. doi: 10.7507/1007-4848.202602002 Copy

1.	Varghese C, Harrison EM, O'Grady G, et al. Artificial intelligence in surgery[J]. Nat Med, 2024, 30(5): 1257-1268.
2.	Zhang K, Yang X, Wang Y, et al. Artificial intelligence in drug development[J]. Nat Med, 2025, 31(1): 45-59.
3.	Ng FYC, Thirunavukarasu AJ, Cheng H, et al. Artificial intelligence education: an evidence-based medicine approach for consumers, translators, and developers[J]. Cell Rep Med, 2023, 4(10): 101230.
4.	Cooper A, Rodman A. AI and medical education: a 21st-century Pandora's box[J]. N Engl J Med, 2023, 389(5): 385-387.
5.	Plackett R, Kassianos AP, Mylan S, et al. The effectiveness of using virtual patient educational tools to improve medical students' clinical reasoning skills: a systematic review[J]. BMC Med Educ, 2022, 22(1): 365.
6.	Plackett R, Kassianos AP, Timmis J, et al. Using virtual patients to explore the clinical reasoning skills of medical students: mixed methods study[J/OL]. J Med Internet Res, 2021, 23(6): e24723. DOI: 10.2196/24723.
7.	劉斌, 王孟慧. 人工智能時代的個性化學習: 內涵、技術支持與實現路徑[J]. 教育探索, 2021, 7: 80-83.Liu B, Wang MH. Personalized learning in the era of artificial intelligence: connotation, technical support and implementation pathways[J]. Educ Explor, 2021, 7: 80-83.
8.	孫宇, 田立偉. 人工智能技術融入大學生學業質量動態監測探析[J]. 教育探索, 2025, 3: 33-36.Sun Y, Tian LW. Integration of artificial intelligence technology into dynamic monitoring of college students' academic quality[J]. Educ Explor, 2025, 3: 33-36.
9.	張如意, 周運翱, 彭迎春. 人工智能賦能醫學教育的協同治理路徑探析[J]. 中華醫學教育雜志, 2025, 45(7): 496-501.Zhang RY, Zhou YA, Peng YC. Collaborative governance pathways for artificial intelligence-empowered medical education[J]. Chin J Med Educ, 2025, 45(7): 496-501.
10.	張俊隆, 張昆松, 馮劭婷, 等. 人工智能賦能醫學教育評價創新生態體系的探索與實踐[J]. 中華醫學教育雜志, 2025, 45(12): 911-915.Zhang JL, Zhang KS, Feng ST, et al. Exploration and practice of an innovative evaluation ecosystem for artificial intelligence-empowered medical education[J]. Chin J Med Educ, 2025, 45(12): 911-915.
11.	Zhang C, Xu J, Tang R, et al. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment[J]. J Hematol Oncol, 2023, 16(1): 114.
12.	瞿星, 楊金銘, 陳滔, 等. ChatGPT對醫學教育模式改變的思考[J]. 四川大學學報(醫學版), 2023, 54(5): 937.Qu X, Yang JM, Chen T, et al. Reflections on the impact of ChatGPT on medical education models[J]. J Sichuan Univ (Med Sci Ed), 2023, 54(5): 937.
13.	Morosky CM, Baecher-Lind L, Chen KT, et al. Practical applications of artificial intelligence chatbots in obstetrics and gynecology medical education[J]. Am J Obstet Gynecol, 2025, 233(1): 4-11.
14.	賈萌, 張瑛琪, 李云峰, 等. 醫學人工智能教育的研究進展[J]. 醫學教育研究與實踐, 2023, 31(1): 1-6.Jia M, Zhang YQ, Li YF, et al. Research progress in medical artificial intelligence education[J]. Med Educ Res Pract, 2023, 31(1): 1-6.
15.	張媛, 吳博涵, 唐彬, 等. 生成式人工智能驅動醫學教育數字化轉型的挑戰與實現路徑[J]. 中華醫學教育雜志, 2025, 45(10): 754-759.Zhang Y, Wu BH, Tang B, et al. Challenges and implementation pathways of generative artificial intelligence-driven digital transformation in medical education[J]. Chin J Med Educ, 2025, 45(10): 754-759.
16.	徐曉巍, 唐琳, 歸航, 等. 數字賦能的醫學教育創新模式探析[J]. 中華醫學教育雜志, 2025, 45(3): 161-166.Xu XW, Tang L, Gui H, et al. Innovative models of digitally empowered medical education[J]. Chin J Med Educ, 2025, 45(3): 161-166.
17.	Cook DA, Triola MM. Virtual patients: a critical literature review and proposed next steps[J]. Med Educ, 2009, 43(4): 303-311.
18.	Cook DA, Erwin PJ, Triola MM. Computerized virtual patients in health professions education: a systematic review and meta-analysis[J]. Acad Med, 2010, 85(10): 1589-1602.
19.	Kononowicz AA, Woodham LA, Edelbring S, et al. Virtual patient simulations in health professions education: systematic review and meta-analysis by the digital health education collaboration[J/OL]. J Med Internet Res, 2019, 21(7): e14676. DOI: 10.2196/14676.
20.	Medlock S, Ploegmakers KJ, Cornet R, et al. Use of an open-source electronic health record to establish a "virtual hospital": a tale of two curricula[J/OL]. Int J Med Inform, 2023, 169: 104907. DOI: 10.1016/j.ijmedinf.2022.104907.
21.	Brügge E, Ricchizzi S, Arenbeck M, et al. Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial[J]. BMC Med Educ, 2024, 24(1): 1391.
22.	?i?ek FE, ülker M, ?zer M, et al. ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial[J]. Postgrad Med J, 2025, 101(1195): 458-463.
23.	Nissen L, Rother JF, Heinemann M, et al. A randomised cross-over trial assessing the impact of AI-generated individual feedback on written online assignments for medical students[J]. Med Teach, 2025, 47(9): 1544-1550.
24.	Schaye V, DiTullio D, Guzman BV, et al. Large language model-based assessment of clinical reasoning documentation in the electronic health record across two institutions: development and validation study[J/OL]. J Med Internet Res, 2025, 27: e67967. DOI: 10.2196/67967.
25.	Ogundiya O, Rahman TJ, Valnarov-Boulter I, et al. Looking back on digital medical education over the last 25 years and looking to the future: narrative review[J/OL]. J Med Internet Res, 2024, 26: e60312. DOI: 10.2196/60312.
26.	Triola MM, Rodman A. Integrating generative artificial intelligence into medical education: curriculum, policy, and governance strategies[J]. Acad Med, 2025, 100(4): 413-418.
27.	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.
28.	熊振興. 生成式人工智能對教學的影響及應對研究—以ChatGPT為例[J]. 教育探索, 2025, 6: 87-93.Xiong ZX. Impact of generative artificial intelligence on teaching and countermeasures: taking ChatGPT as an example[J]. Educ Explor, 2025, 6: 87-93.
29.	殷文軒. 生成式人工智能賦能大學教學的作用機制與推進路徑—基于斯坦福大學的案例分析[J]. 教育探索, 2025, 4: 33-39.Yin WX. Mechanisms and advancement pathways of generative artificial intelligence-empowered university teaching: a case study of Stanford University[J]. Educ Explor, 2025, 4: 33-39.
30.	Wark S, Drovandi A, McGee RG, et al. How and when should clinical reasoning be taught in undergraduate medicine: a systematic review and meta-analyses[J]. Perspect Med Educ, 2025, 14(1): 1021-1042.
31.	Norman G, Pelaccia T, Wyer P, et al. Dual process models of clinical reasoning: the central role of knowledge in diagnostic expertise[J]. J Eval Clin Pract, 2024, 30(5): 788-796.
32.	Hawks MK, Maciuba JM, Merkebu J, et al. Clinical reasoning curricula in preclinical undergraduate medical education: a scoping review[J]. Acad Med, 2023, 98(8): 958-965.
33.	Kiesewetter J, Sailer M, Jung VM, et al. Learning clinical reasoning: how virtual patient case format and prior knowledge interact[J]. BMC Med Educ, 2020, 20(1): 73.
34.	Castro MABE, de Almeida RLM, Lucchetti ALG, et al. The use of feedback in improving the knowledge, attitudes and skills of medical students: a systematic review and meta-analysis of randomized controlled trials[J]. Med Sci Educ, 2021, 31(6): 2093-2104.
35.	Natesan S, Jordan J, Sheng A, et al. Feedback in medical education: an evidence-based guide to best practices from the council of residency directors in emergency medicine[J]. West J Emerg Med, 2023, 24(3): 479-494.
36.	Toofaninejad E, Dawson S, Sohrabi S, et al. Exploring the transformative potential of learning analytics in medical education: a systematic review[J]. J Adv Med Educ Prof, 2025, 13(1): 12-24.
37.	Bojic I, Mammadova M, Ang CS, et al. Empowering health care education through learning analytics: in-depth scoping review[J/OL]. J Med Internet Res, 2023, 25: e41671. DOI: 10.2196/41671.
38.	Tozsin A, Ucmak H, Soyturk S, et al. The role of artificial intelligence in medical education: a systematic review[J]. Surg Innov, 2024, 31(4): 415-423.
39.	Malik TG, Mahboob U, Khan RA, et al. Virtual patients versus standardized patients for improving clinical reasoning skills in ophthalmology residents: a randomized controlled trial[J]. BMC Med Educ, 2024, 24(1): 429.
40.	Dávidovics A, Dávidovics K, Hillebrand P, et al. Virtual patient simulation to enhance medical students' clinical communication and decision-making skills: a pilot study[J]. BMC Med Educ, 2025, 26(1): 171.
41.	Jiang H, Vimalesvaran S, Wang JK, et al. Virtual reality in medical students' education: scoping review[J/OL]. JMIR Med Educ, 2022, 8(1): e34860. DOI: 10.2196/34860.
42.	Mohd Noor MN, Cockburn JG, Foong CC, et al. Developing a framework for medical student feedback literacy using a triangulated thematic analysis[J]. Ann Med, 2025, 57(1): 2520395.
43.	Hudon A, Kiepura B, Pelletier M, et al. Using ChatGPT in psychiatry to design script concordance tests in undergraduate medical education: mixed methods study[J/OL]. JMIR Med Educ, 2024, 10: e54067. DOI: 10.2196/54067.
44.	Elendu C, Amaechi DC, Okatta AU, et al. The impact of simulation-based training in medical education: a review[J/OL]. Medicine (Baltimore), 2024, 103(27): e38813. DOI: 10.1097/MD.0000000000038813.
45.	Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[J]. Nat Med, 2020, 26(9): 1364-1374.
46.	Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension[J/OL]. BMJ, 2020, 370: m3210. DOI: 10.1136/bmj.m3210.
47.	Wisniewski B, Zierer K, Hattie J. The power of feedback revisited: a meta-analysis of educational feedback research[J]. Front Psychol, 2020, 10: 3087.
48.	Goisauf M, Cano Abadía M, Akyüz K, et al. Trust, trustworthiness, and the future of medical AI: outcomes of an interdisciplinary expert workshop[J/OL]. J Med Internet Res, 2025, 27: e71236. DOI: 10.2196/71236.
49.	Lekadir K, Frangi AF, Porras AR, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare[J/OL]. BMJ, 2025, 388: e081554. DOI: 10.1136/bmj-2024-081554.
50.	Soledad AR, Catalina ZS, Scarlett VC, et al. Using the OSCE to assess medical students' communication and clinical reasoning during five years of restricted clinical practice[J]. BMC Med Educ, 2025, 25(1): 608.
51.	Ong T, Lee H, Somay S, et al. Evaluation of a novel formative objective structured clinical examination for clinical reasoning processes in undergraduate medical education: a pilot study[J/OL]. Acad Med, 2026, wvag008. DOI: 10.1093/acamed/wvag008.
52.	Fink MC, Heitzmann N, Siebeck M, et al. Learning to diagnose accurately through virtual patients: do reflection phases have an added benefit[J]? BMC Med Educ, 2021, 21(1): 523.
53.	Vasey B, Novak A, Ather S, et al. DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology[J]. Clin Radiol, 2023, 78(2): 130-136.
54.	Gallifant J, Afshar M, Ameen S, et al. The TRIPOD-LLM reporting guideline for studies using large language models[J]. Nat Med, 2025, 31(1): 60-69.

1. Varghese C, Harrison EM, O'Grady G, et al. Artificial intelligence in surgery[J]. Nat Med, 2024, 30(5): 1257-1268.
2. Zhang K, Yang X, Wang Y, et al. Artificial intelligence in drug development[J]. Nat Med, 2025, 31(1): 45-59.
3. Ng FYC, Thirunavukarasu AJ, Cheng H, et al. Artificial intelligence education: an evidence-based medicine approach for consumers, translators, and developers[J]. Cell Rep Med, 2023, 4(10): 101230.
4. Cooper A, Rodman A. AI and medical education: a 21st-century Pandora's box[J]. N Engl J Med, 2023, 389(5): 385-387.
5. Plackett R, Kassianos AP, Mylan S, et al. The effectiveness of using virtual patient educational tools to improve medical students' clinical reasoning skills: a systematic review[J]. BMC Med Educ, 2022, 22(1): 365.
6. Plackett R, Kassianos AP, Timmis J, et al. Using virtual patients to explore the clinical reasoning skills of medical students: mixed methods study[J/OL]. J Med Internet Res, 2021, 23(6): e24723. DOI: 10.2196/24723.
7. 劉斌, 王孟慧. 人工智能時代的個性化學習: 內涵、技術支持與實現路徑[J]. 教育探索, 2021, 7: 80-83.Liu B, Wang MH. Personalized learning in the era of artificial intelligence: connotation, technical support and implementation pathways[J]. Educ Explor, 2021, 7: 80-83.
8. 孫宇, 田立偉. 人工智能技術融入大學生學業質量動態監測探析[J]. 教育探索, 2025, 3: 33-36.Sun Y, Tian LW. Integration of artificial intelligence technology into dynamic monitoring of college students' academic quality[J]. Educ Explor, 2025, 3: 33-36.
9. 張如意, 周運翱, 彭迎春. 人工智能賦能醫學教育的協同治理路徑探析[J]. 中華醫學教育雜志, 2025, 45(7): 496-501.Zhang RY, Zhou YA, Peng YC. Collaborative governance pathways for artificial intelligence-empowered medical education[J]. Chin J Med Educ, 2025, 45(7): 496-501.
10. 張俊隆, 張昆松, 馮劭婷, 等. 人工智能賦能醫學教育評價創新生態體系的探索與實踐[J]. 中華醫學教育雜志, 2025, 45(12): 911-915.Zhang JL, Zhang KS, Feng ST, et al. Exploration and practice of an innovative evaluation ecosystem for artificial intelligence-empowered medical education[J]. Chin J Med Educ, 2025, 45(12): 911-915.
11. Zhang C, Xu J, Tang R, et al. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment[J]. J Hematol Oncol, 2023, 16(1): 114.
12. 瞿星, 楊金銘, 陳滔, 等. ChatGPT對醫學教育模式改變的思考[J]. 四川大學學報(醫學版), 2023, 54(5): 937.Qu X, Yang JM, Chen T, et al. Reflections on the impact of ChatGPT on medical education models[J]. J Sichuan Univ (Med Sci Ed), 2023, 54(5): 937.
13. Morosky CM, Baecher-Lind L, Chen KT, et al. Practical applications of artificial intelligence chatbots in obstetrics and gynecology medical education[J]. Am J Obstet Gynecol, 2025, 233(1): 4-11.
14. 賈萌, 張瑛琪, 李云峰, 等. 醫學人工智能教育的研究進展[J]. 醫學教育研究與實踐, 2023, 31(1): 1-6.Jia M, Zhang YQ, Li YF, et al. Research progress in medical artificial intelligence education[J]. Med Educ Res Pract, 2023, 31(1): 1-6.
15. 張媛, 吳博涵, 唐彬, 等. 生成式人工智能驅動醫學教育數字化轉型的挑戰與實現路徑[J]. 中華醫學教育雜志, 2025, 45(10): 754-759.Zhang Y, Wu BH, Tang B, et al. Challenges and implementation pathways of generative artificial intelligence-driven digital transformation in medical education[J]. Chin J Med Educ, 2025, 45(10): 754-759.
16. 徐曉巍, 唐琳, 歸航, 等. 數字賦能的醫學教育創新模式探析[J]. 中華醫學教育雜志, 2025, 45(3): 161-166.Xu XW, Tang L, Gui H, et al. Innovative models of digitally empowered medical education[J]. Chin J Med Educ, 2025, 45(3): 161-166.
17. Cook DA, Triola MM. Virtual patients: a critical literature review and proposed next steps[J]. Med Educ, 2009, 43(4): 303-311.
18. Cook DA, Erwin PJ, Triola MM. Computerized virtual patients in health professions education: a systematic review and meta-analysis[J]. Acad Med, 2010, 85(10): 1589-1602.
19. Kononowicz AA, Woodham LA, Edelbring S, et al. Virtual patient simulations in health professions education: systematic review and meta-analysis by the digital health education collaboration[J/OL]. J Med Internet Res, 2019, 21(7): e14676. DOI: 10.2196/14676.
20. Medlock S, Ploegmakers KJ, Cornet R, et al. Use of an open-source electronic health record to establish a "virtual hospital": a tale of two curricula[J/OL]. Int J Med Inform, 2023, 169: 104907. DOI: 10.1016/j.ijmedinf.2022.104907.
21. Brügge E, Ricchizzi S, Arenbeck M, et al. Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial[J]. BMC Med Educ, 2024, 24(1): 1391.
22. ?i?ek FE, ülker M, ?zer M, et al. ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial[J]. Postgrad Med J, 2025, 101(1195): 458-463.
23. Nissen L, Rother JF, Heinemann M, et al. A randomised cross-over trial assessing the impact of AI-generated individual feedback on written online assignments for medical students[J]. Med Teach, 2025, 47(9): 1544-1550.
24. Schaye V, DiTullio D, Guzman BV, et al. Large language model-based assessment of clinical reasoning documentation in the electronic health record across two institutions: development and validation study[J/OL]. J Med Internet Res, 2025, 27: e67967. DOI: 10.2196/67967.
25. Ogundiya O, Rahman TJ, Valnarov-Boulter I, et al. Looking back on digital medical education over the last 25 years and looking to the future: narrative review[J/OL]. J Med Internet Res, 2024, 26: e60312. DOI: 10.2196/60312.
26. Triola MM, Rodman A. Integrating generative artificial intelligence into medical education: curriculum, policy, and governance strategies[J]. Acad Med, 2025, 100(4): 413-418.
27. 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.
28. 熊振興. 生成式人工智能對教學的影響及應對研究—以ChatGPT為例[J]. 教育探索, 2025, 6: 87-93.Xiong ZX. Impact of generative artificial intelligence on teaching and countermeasures: taking ChatGPT as an example[J]. Educ Explor, 2025, 6: 87-93.
29. 殷文軒. 生成式人工智能賦能大學教學的作用機制與推進路徑—基于斯坦福大學的案例分析[J]. 教育探索, 2025, 4: 33-39.Yin WX. Mechanisms and advancement pathways of generative artificial intelligence-empowered university teaching: a case study of Stanford University[J]. Educ Explor, 2025, 4: 33-39.
30. Wark S, Drovandi A, McGee RG, et al. How and when should clinical reasoning be taught in undergraduate medicine: a systematic review and meta-analyses[J]. Perspect Med Educ, 2025, 14(1): 1021-1042.
31. Norman G, Pelaccia T, Wyer P, et al. Dual process models of clinical reasoning: the central role of knowledge in diagnostic expertise[J]. J Eval Clin Pract, 2024, 30(5): 788-796.
32. Hawks MK, Maciuba JM, Merkebu J, et al. Clinical reasoning curricula in preclinical undergraduate medical education: a scoping review[J]. Acad Med, 2023, 98(8): 958-965.
33. Kiesewetter J, Sailer M, Jung VM, et al. Learning clinical reasoning: how virtual patient case format and prior knowledge interact[J]. BMC Med Educ, 2020, 20(1): 73.
34. Castro MABE, de Almeida RLM, Lucchetti ALG, et al. The use of feedback in improving the knowledge, attitudes and skills of medical students: a systematic review and meta-analysis of randomized controlled trials[J]. Med Sci Educ, 2021, 31(6): 2093-2104.
35. Natesan S, Jordan J, Sheng A, et al. Feedback in medical education: an evidence-based guide to best practices from the council of residency directors in emergency medicine[J]. West J Emerg Med, 2023, 24(3): 479-494.
36. Toofaninejad E, Dawson S, Sohrabi S, et al. Exploring the transformative potential of learning analytics in medical education: a systematic review[J]. J Adv Med Educ Prof, 2025, 13(1): 12-24.
37. Bojic I, Mammadova M, Ang CS, et al. Empowering health care education through learning analytics: in-depth scoping review[J/OL]. J Med Internet Res, 2023, 25: e41671. DOI: 10.2196/41671.
38. Tozsin A, Ucmak H, Soyturk S, et al. The role of artificial intelligence in medical education: a systematic review[J]. Surg Innov, 2024, 31(4): 415-423.
39. Malik TG, Mahboob U, Khan RA, et al. Virtual patients versus standardized patients for improving clinical reasoning skills in ophthalmology residents: a randomized controlled trial[J]. BMC Med Educ, 2024, 24(1): 429.
40. Dávidovics A, Dávidovics K, Hillebrand P, et al. Virtual patient simulation to enhance medical students' clinical communication and decision-making skills: a pilot study[J]. BMC Med Educ, 2025, 26(1): 171.
41. Jiang H, Vimalesvaran S, Wang JK, et al. Virtual reality in medical students' education: scoping review[J/OL]. JMIR Med Educ, 2022, 8(1): e34860. DOI: 10.2196/34860.
42. Mohd Noor MN, Cockburn JG, Foong CC, et al. Developing a framework for medical student feedback literacy using a triangulated thematic analysis[J]. Ann Med, 2025, 57(1): 2520395.
43. Hudon A, Kiepura B, Pelletier M, et al. Using ChatGPT in psychiatry to design script concordance tests in undergraduate medical education: mixed methods study[J/OL]. JMIR Med Educ, 2024, 10: e54067. DOI: 10.2196/54067.
44. Elendu C, Amaechi DC, Okatta AU, et al. The impact of simulation-based training in medical education: a review[J/OL]. Medicine (Baltimore), 2024, 103(27): e38813. DOI: 10.1097/MD.0000000000038813.
45. Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[J]. Nat Med, 2020, 26(9): 1364-1374.
46. Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension[J/OL]. BMJ, 2020, 370: m3210. DOI: 10.1136/bmj.m3210.
47. Wisniewski B, Zierer K, Hattie J. The power of feedback revisited: a meta-analysis of educational feedback research[J]. Front Psychol, 2020, 10: 3087.
48. Goisauf M, Cano Abadía M, Akyüz K, et al. Trust, trustworthiness, and the future of medical AI: outcomes of an interdisciplinary expert workshop[J/OL]. J Med Internet Res, 2025, 27: e71236. DOI: 10.2196/71236.
49. Lekadir K, Frangi AF, Porras AR, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare[J/OL]. BMJ, 2025, 388: e081554. DOI: 10.1136/bmj-2024-081554.
50. Soledad AR, Catalina ZS, Scarlett VC, et al. Using the OSCE to assess medical students' communication and clinical reasoning during five years of restricted clinical practice[J]. BMC Med Educ, 2025, 25(1): 608.
51. Ong T, Lee H, Somay S, et al. Evaluation of a novel formative objective structured clinical examination for clinical reasoning processes in undergraduate medical education: a pilot study[J/OL]. Acad Med, 2026, wvag008. DOI: 10.1093/acamed/wvag008.
52. Fink MC, Heitzmann N, Siebeck M, et al. Learning to diagnose accurately through virtual patients: do reflection phases have an added benefit[J]? BMC Med Educ, 2021, 21(1): 523.
53. Vasey B, Novak A, Ather S, et al. DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology[J]. Clin Radiol, 2023, 78(2): 130-136.
54. Gallifant J, Afshar M, Ameen S, et al. The TRIPOD-LLM reporting guideline for studies using large language models[J]. Nat Med, 2025, 31(1): 60-69.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Exploration and effect evaluation of a clinical teaching model in respiratory medicine based on the "LungSmart" smart healthcare system

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content