From "standardized patients" to "digital twin patients": Disciplinary logic and technical divergence of five patient modeling paradigms_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHU Xingyuan ¹ , WANG Jiaqing ^2,3 , YANG Zhongfang ⁴ , HUANG Zong'an ⁵ ,  ZHU Zheng ^2,6

1. School of Nursing, Dali University, Dali, 671000, Yunnan, P. R. China;
2. Yulin AI-Enhanced Healthcare Lab, Shanghai, 200032, P. R. China;
3. Shanghai Lianhong Technology Co., Ltd. Shanghai, 200032, P. R. China;
4. School of Nursing, Medical College of Soochow University, Suzhou, 215006, Jiangsu, P. R. China;
5. School of Computer Science and Technology, Fudan University, Shanghai, 200433, P. R. China;
6. School of Nursing, Fudan University, Shanghai, 200032, P. R. China;

Corresponding?author：

ZHU Zheng, Email: zhengzhu@fudan.edu.cn

Keywords：

Large language models; patient modeling; medical simulation; AI patients; digital twin patients

DOI：

10.7507/1007-4848.202603001

Video：

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With the rapid development of large language models and related technologies, various forms of patient modeling have emerged, including virtual patients, synthetic patients, artificial intelligence patients, and digital twin patients. Although these forms all share the core objective of patient simulation, they exhibit significant differences in theoretical ontology, data sources, modeling logic, and application orientation. Based on the abstraction levels of simulation objects and system mapping logic, this study systematically compares five paradigms—standardized patients, virtual patients, synthetic patients, AI patients, and digital twin patients—to clarify their conceptual boundaries and technical positioning. The findings indicate that these paradigms represent distinct modeling pathways: behavioral reproduction, scenario simulation, data generation, cognitive interaction, and individual system mapping, respectively. By constructing a multidimensional comparative framework, this study provides a theoretical foundation for the conceptual standardization of patient modeling and the strategic deployment of intelligent healthcare systems.

Citation： ZHU Xingyuan, WANG Jiaqing, YANG Zhongfang, HUANG Zong'an, ZHU Zheng. From "standardized patients" to "digital twin patients": Disciplinary logic and technical divergence of five patient modeling paradigms. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2026, 33(7): 1023-1033. doi: 10.7507/1007-4848.202603001 Copy

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36.	Fernández-Alcántara M, Escribano S, Juliá-Sanchis R, et al. Virtual simulation tools for communication skills training in health care professionals: literature review. JMIR Med Educ, 2025, 11: e63082.
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1. 丁炎明, 吳欣娟, 劉飛, 等. 三級綜合醫院新護士規范化培訓的現狀調查. 中華護理雜志, 2020, 55(3): 331-336.Ding YM, Wu XJ, Liu F, et al. Current situation investigation on standardized training of new nurses in tertiary general hospitals. Chin J Nurs, 2020, 55(3): 331-336.
2. Barrows HS. An overview of the uses of standardized patients for teaching and evaluating clinical skills. AAMC. Acad Med, 1993, 68(6): 443-451.
3. 李欽楠, 吳健雄, 王菁, 等. 標準化患者在衛生服務質量評估中應用研究進展. 中國公共衛生, 2022, 38(12): 1619-1622.Li QN, Wu JX, Wang J, et al. Application progress of standardized patients in healthcare quality assessment. Chin J Public Health, 2022, 38(12): 1619-1622.
4. McCarthy DM, Powell RE, Cameron KA, et al. Simulation-based mastery learning compared to standard education for discussing diagnostic uncertainty with patients in the emergency department: a randomized controlled trial. BMC Med Educ, 2020, 20(1): 49.
5. Wendling AL, Halan S, Tighe P, et al. Virtual humans versus standardized patients: which lead residents to more correct diagnoses? Acad Med, 2011, 86(3): 384-388.
6. Abbasian M, Khatibi E, Azimi I, et al. Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI. NPJ Digit Med, 2024, 7(1): 82.
7. Katsoulakis E, Wang Q, Wu H, et al. Digital twins for health: a scoping review. NPJ Digit Med, 2024, 7(1): 77.
8. Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health, 2023, 2(2): e0000198.
9. Abd-Alrazaq A, AlSaad R, Alhuwail D, et al. Large language models in medical education: opportunities, challenges, and future directions. JMIR Med Educ, 2023, 9: e48291.
10. Xu X, Chen Y, Miao J. Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review. J Educ Eval Health Prof, 2024, 21: 6.
11. Holderried F, Stegemann-Philipps C, Herrmann-Werner A, et al. A language model-powered simulated patient with automated feedback for history taking: prospective study. JMIR Med Educ, 2024, 10: e59213.
12. Cook DA. Creating virtual patients using large language models: scalable, global, and low cost. Med Teach, 2025, 47(1): 40-42.
13. Gutiérrez Maquilón R, Uhl J, Schrom-Feiertag H, et al. Integrating GPT-based AI into virtual patients to facilitate communication training among medical first responders: usability study of mixed reality simulation. JMIR Form Res, 2024, 8: e58623.
14. Tudor BH, Shargo R, Gray GM, et al. A scoping review of human digital twins in healthcare applications and usage patterns. NPJ Digit Med, 2025, 8(1): 587.
15. Sadée C, Testa S, Barba T, et al. Medical digital twins: enabling precision medicine and medical artificial intelligence. Lancet Digit Health, 2025, 7(7): e100864.
16. Zhang K, Zhou HY, Baptista-Hon DT, et al. Concepts and applications of digital twins in healthcare and medicine. Patterns (N Y), 2024, 5(8): 101028.
17. Ringeval M, Etindele Sosso FA, Cousineau M, et al. Advancing health care with digital twins: meta-review of applications and implementation challenges. J Med Internet Res, 2025, 27: e69544.
18. Saratkar SY, Langote M, Kumar P, et al. Digital twin for personalized medicine development. Front Digit Health, 2025, 7: 1583466.
19. Wilbur K, Elmubark A, Shabana S. Systematic review of standardized patient use in continuing medical education. J Contin Educ Health Prof, 2018, 38(1): 3-10.
20. 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 Med Internet Res, 2019, 21(7): e14676.
21. Pezoulas VC, Zaridis DI, Mylona E, et al. Synthetic data generation methods in healthcare: a review on open-source tools and methods. Comput Struct Biotechnol J, 2024, 23: 2892-2910.
22. Hamilton A, Molzahn A, McLemore K. The evolution from standardized to virtual patients in medical education. Cureus, 2024, 16(10): e71224.
23. Issenberg SB, McGaghie WC, Petrusa ER, et al. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach, 2005, 27(1): 10-28.
24. Rujas M, Martín Gómez Del Moral Herranz R, Fico G, et al. Synthetic data generation in healthcare: a scoping review of reviews on domains, motivations, and future applications. Int J Med Inform, 2025, 195: 105763.
25. Kamel Boulos MN, Zhang P. Digital twins: from personalised medicine to precision public health. J Pers Med, 2021, 11(8): 745.
26. Cleland JA, Abe K, Rethans JJ. The use of simulated patients in medical education: AMEE guide no 42. Med Teach, 2009, 31(6): 477-486.
27. Ghaffari F, Langarizadeh M, Nabovati E, et al. Effectiveness of ChatGPT for clinical scenario generation: a qualitative study. Arch Acad Emerg Med, 2025, 13(1): e49.
28. Maicher KR, Zimmerman L, Wilcox B, et al. Using virtual standardized patients to accurately assess information gathering skills in medical students. Med Teach, 2019, 41(9): 1053-1059.
29. Maicher KR, Stiff A, Scholl M, et al. Artificial intelligence in virtual standardized patients: combining natural language understanding and rule based dialogue management to improve conversational fidelity. Med Teach, 2023, 45(3): 279-285.
30. Wang C, Li S, Lin N, et al. Application of large language models in medical training evaluation-using ChatGPT as a standardized patient: multimetric assessment. J Med Internet Res, 2025, 27: e59435.
31. Cook DA, Erwin PJ, Triola MM. Computerized virtual patients in health professions education: a systematic review and meta-analysis. Acad Med, 2010, 85(10): 1589-1602.
32. Fors UG, Muntean V, Botezatu M, et al. Cross-cultural use and development of virtual patients. Med Teach, 2009, 31(8): 732-738.
33. Chen PJ, Liou WK. ChatGPT-driven interactive virtual reality communication simulation in obstetric nursing: a mixed-methods study. Nurse Educ Pract, 2025, 85: 104383.
34. Zhu XT, Cheerman H, Cheng M, et al. Designing VR simulation system for clinical communication training with LLMs-based embodied conversational agents. In: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM; 2025. Article 181.
35. Lee J, Kim H, Kim KH, et al. Effective virtual patient simulators for medical communication training: a systematic review. Med Educ, 2020, 54(9): 786-795.
36. Fernández-Alcántara M, Escribano S, Juliá-Sanchis R, et al. Virtual simulation tools for communication skills training in health care professionals: literature review. JMIR Med Educ, 2025, 11: e63082.
37. Holderried F, Sonanini A, Philipps A, et al. Using AI to train future clinicians in depression assessment: feasibility study. JMIR Med Educ, 2026, 12: e87102.
38. Li D, Lebai Lutfi S. Large language model-based virtual patient systems for history-taking in medical education: comprehensive systematic review. JMIR Med Inform, 2026, 14: e79039.
39. Chen RJ, Lu MY, Chen TY, et al. Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng, 2021, 5(6): 493-497.
40. Yoon J, Mizrahi M, Ghalaty NF, et al. EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records. NPJ Digit Med, 2023, 6(1): 141.
41. 田猛, 陳博, 郭安, 等. 基于擴散模型生成保護隱私的合成電子健康記錄時間序列的可靠方法. 中華醫學雜志, 2024, 31(11): 2529-2539.Tian M, Chen B, Guo A, et al. Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models. Nat Med J China, 2024, 31(11): 2529-2539.
42. Giuffrè M, Shung DL. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ Digit Med, 2023, 6(1): 186.
43. Gonzales A, Guruswamy G, Smith SR. Synthetic data in health care: a narrative review. PLOS Digit Health, 2023, 2(1): e0000082.
44. Liu Y, Acharya UR, Tan JH. Preserving privacy in healthcare: a systematic review of deep learning approaches for synthetic data generation. Comput Methods Programs Biomed, 2025, 260: 108571.
45. Eden R, Chukwudi I, Bain C, et al. A scoping review of the governance of federated learning in healthcare. NPJ Digit Med, 2025, 8(1): 427.
46. Grazhdanski G, Vasilev V, Vassileva S, et al. SynthMedic: utilizing large language models for synthetic discharge summary generation, correction and validation. J Biomed Inform, 2025, 170: 104906.
47. Smolyak D, Bjarnadóttir MV, Crowley K, et al. Large language models and synthetic health data: progress and prospects. JAMIA Open, 2024, 7(4): ooae114.
48. Litake O, Park BH, Tully JL, et al. Constructing synthetic datasets with generative artificial intelligence to train large language models to classify acute renal failure from clinical notes. J Am Med Inform Assoc, 2024, 31(6): 1404-1410.
49. Sarkar AR, Chuang YS, Mohammed N, et al. De-identification is not enough: a comparison between de-identified and synthetic clinical notes. Sci Rep, 2024, 14(1): 29669.
50. Huang R, Wu H, Yuan Y, et al. Evaluation and bias analysis of large language models in generating synthetic electronic health records: comparative study. J Med Internet Res, 2025, 27: e65317.
51. Yu H, Zhou J, Li L, et al. Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education. Commun Med (Lond), 2025, 6(1): 27.
52. Zhang B, Liu X, Wang Y, et al. Human or LLM as standardized patients? A comparative study for medical education. arXiv, 2025. Preprint. Accessed 2026-03-15.
53. Liu H, Liao Y, Ou S, et al. Med-PMC: medical personalized multi-modal consultation with a proactive ask-first-observe-next paradigm. arXiv, 2024: 2408.08693.
54. Dollis JS, Brito IA, F?rber FB, et al. When avatars have personality: effects on engagement and communication in immersive medical training. arXiv, 2025: 2509.14132.
55. Wang R, Milani S, Chiu JC, et al. PATIENT-ψ: using large language models to simulate patients for training mental health professionals. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024: 12772-12797.
56. Voigt H, Sugamiya Y, Lawonn K, et al. LLM-powered virtual patient agents for interactive clinical skills training with automated feedback. arXiv, 2025: 2508.13943.
57. Borg A, Georg C, Jobs B, et al. Virtual patient simulations using social robotics combined with large language models for clinical reasoning training in medical education: mixed methods study. J Med Internet Res, 2025, 27: e63312.
58. Yuan Y, He J, Wang F, et al. AI agent as a simulated patient for history-taking training in clinical clerkship: an example in stomatology. Glob Med Educ, 2025, 2: 171-177.
59. Cabrera Lozoya D, Conway M, Sebastiano De Duro E, et al. Leveraging large language models for simulated psychotherapy client interactions: development and usability study of Client101. JMIR Med Educ, 2025, 11: e68056.
60. Fan Z, Wei L, Tang J, et al. AI Hospital: benchmarking large language models in a multi-agent medical interaction simulator. In: Proceedings of the 31st International Conference on Computational Linguistics. Abu Dhabi, UAE, 2025: 10183-10213.
61. Wu J, Liang X, Bai X, et al. Surgbox: agent-driven operating room sandbox with surgery copilot. In: 2024 IEEE International Conference on Big Data (BigData). Piscataway, NJ, USA: IEEE, 2024: 2041-2048.
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Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

From "standardized patients" to "digital twin patients": Disciplinary logic and technical divergence of five patient modeling paradigms

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