Assessing ChatGPT’s capability as a new age standardized patient: qualitative study
Open accessAbstract
Background: Standardized patients (SPs) have been crucial in medical education, offering realistic patient interactions to students. Despite their benefits, SP training is resource-intensive and access can be limited. Advances in artificial intelligence (AI), particularly with large language models such as ChatGPT, present new opportunities for virtual SPs, potentially addressing these limitations. Objectives: This study aims to assess medical students’ perceptions and experiences of using ChatGPT as an SP and to evaluate ChatGPT’s effectiveness in performing as a virtual SP in a medical school setting. Methods: This qualitative study, approved by the American University of Antigua Institutional Review Board, involved 9 students (5 females and 4 males, aged 22‐48 years) from the American University of Antigua College of Medicine. Students were observed during a live role-play, interacting with ChatGPT as an SP using a predetermined prompt. A structured 15-question survey was administered before and after the interaction. Thematic analysis was conducted on the transcribed and coded responses, with inductive category formation. Results: Thematic analysis identified key themes preinteraction including technology limitations (eg, prompt engineering difficulties), learning efficacy (eg, potential for personalized learning and reduced interview stress), verisimilitude (eg, absence of visual cues), and trust (eg, concerns about AI accuracy). Postinteraction, students noted improvements in prompt engineering, some alignment issues (eg, limited responses on sensitive topics), maintained learning efficacy (eg, convenience and repetition), and continued verisimilitude challenges (eg, lack of empathy and nonverbal cues). No significant trust issues were reported postinteraction. Despite some limitations, students found ChatGPT as a valuable supplement to traditional SPs, enhancing practice flexibility and diagnostic skills. Conclusions: ChatGPT can effectively augment traditional SPs in medical education, offering accessible, flexible practice opportunities. However, it cannot fully replace human SPs due to limitations in verisimilitude and prompt engineering challenges. Integrating prompt engineering into medical curricula and continuous advancements in AI are recommended to enhance the use of virtual SPs.
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Cites
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
Qualitative Content Analysis: Theoretical Background and Procedures
Mayring, P. (2014) · Advances in mathematics education · doi:10.1007/978-94-017-9181-6_13 · Cited by 2,349 · see citing works
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · see citing works
Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial
Meskó, B. (2023) · Journal of Medical Internet Research · doi:10.2196/50638 · Cited by 709 · see citing works
Boscardin, C. K., Gin, B., Golde, P. B., & Hauer, K. E. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005439 · Cited by 533 · cited 2× · see citing works
“Embedding these competencies into existing core courses, rather than as electives, will ensure comprehensive and equitable learning opportunities [43,44,46,48].”
The use of simulated patients in medical education: AMEE Guide No 42
Cleland, J. A., Abe, K., & Rethans, J.-J. (2009) · Medical Teacher · doi:10.1080/01421590903002821 · Cited by 509 · see citing works
Towards Generalist Biomedical AI
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2024) · NEJM AI · doi:10.1056/aioa2300138 · Cited by 483 · see citing works
Towards Generalist Biomedical AI
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.14334 · Cited by 483 · see citing works
What do medical students actually need to know about artificial intelligence?
McCoy, L. G., Nagaraj, S., Morgado, F., Harish, V., Das, S., & Celi, L. A. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0294-7 · Cited by 240 · see citing works
Virtual Patient Simulation at U.S. and Canadian Medical Schools
Huang, G. C., Reynolds, R., & Candler, C. S. (2007) · Academic Medicine · doi:10.1097/acm.0b013e31803e8a0a · Cited by 235 · see citing works
The Role of Large Language Models in Medical Education: Applications and Implications
Safranek, C. W., Sidamon-Eristoff, A. E., Gilson, A., & Chartash, D. (2023) · JMIR Medical Education · doi:10.2196/50945 · Cited by 233 · see citing works
Prompt Engineering in Medical Education
Heston, T. F., & Khun, C. (2023) · International Medical Education · doi:10.3390/ime2030019 · Cited by 232 · see citing works
Moldt, J.-A., Festl-Wietek, T., Madany Mamlouk, A., Nieselt, K., Fuhl, W., & Herrmann-Werner, A. (2023) · Medical Education Online · doi:10.1080/10872981.2023.2182659 · Cited by 186 · see citing works
Temsah, O., Khan, S., Chaiah, Y., Senjab, A., Alhasan, K. A., Jamal, A. A., Aljamaan, F., Malki, K. H., Halwani, R., Al‐Tawfiq, J. A., et al. (2023) · Cureus · doi:10.7759/cureus.37281 · Cited by 182 · see citing works
Kavadella, A., da Silva, M. A. D., Kaklamanos, E. G., Stamatopoulos, V., & Giannakopoulos, K. (2023) · JMIR Medical Education · doi:10.2196/51344 · Cited by 169 · see citing works
Holderried, F., Stegemann–Philipps, C., Herschbach, L., Moldt, J.-A., Nevins, A., Griewatz, J., Holderried, M., Herrmann-Werner, A., Festl-Wietek, T., & Mahling, M. (2024) · JMIR Medical Education · doi:10.2196/53961 · Cited by 156 · see citing works
Towards Conversational Diagnostic AIPreprint
Tu, T., Palepu, A. K., Schaekermann, M., Saab, K. K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Tomašev, N., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.05654 · Cited by 114 · see citing works
Embracing ChatGPT for Medical Education: Exploring Its Impact on Doctors and Medical Students
Wu, Y., Zheng, Y., Feng, B., Yang, Y., Kang, K., & Zhao, A. (2024) · JMIR Medical Education · doi:10.2196/52483 · Cited by 92 · see citing works
Suárez, A., Velasco, A. A., García, V. D., Freire, Y., & Algar, J. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19148735 · Cited by 88 · see citing works
Current status of ChatGPT use in medical education: potentials, challenges, and strategies
Xu, T., Weng, H., Liu, F., Yang, L., Luo, Y., Ding, Z., & Wang, Q. (2024) · Journal of Medical Internet Research · doi:10.2196/57896 · Cited by 84 · see citing works
Standardized Patients in Medical Education: A Review of the Literature
Flanagan, O. L., & Cummings, K. M. (2023) · Cureus · doi:10.7759/cureus.42027 · Cited by 81 · see citing works
Proof of Concept: Using ChatGPT to Teach Emergency Physicians How to Break Bad News
Webb, J. J. (2023) · Cureus · doi:10.7759/cureus.38755 · Cited by 80 · see citing works
Magalhães Araujo, S., & Cruz-Correia, R. (2024) · JMIR Medical Education · doi:10.2196/51151 · Cited by 72 · see citing works
Weidener, L., & Fischer, M. (2023) · JMIR Medical Education · doi:10.2196/46428 · Cited by 70 · see citing works
Medical education empowered by generative artificial intelligence large language models
Jowsey, T., Stokes-Parish, J. B., Singleton, R. S., & Todorovic, M. (2023) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2023.08.012 · Cited by 61 · see citing works
Gupta, N., Khatri, K., Malik, Y., Lakhani, A., Kanwal, A., Aggarwal, S., & Dahuja, A. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06592-8 · Cited by 46 · see citing works
Gray, M. M., Baird, A. J., Sawyer, T., James, J., DeBroux, T., Bartlett, M. J., Krick, J., & Umoren, R. A. (2023) · JMIR Medical Education · doi:10.2196/50705 · Cited by 43 · see citing works
ChatGPT: when the artificial intelligence meets standardized patients in clinical training
Liu, X., Wu, C., Lai, R., Lin, H., Xu, Y., Lin, Y., & Zhang, W. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04314-0 · Cited by 37 · see citing works
Mühling, T., Schreiner, V., Appel, M., Leutritz, T., & Koenig, S. (2024) · Journal of Medical Internet Research · doi:10.2196/55066 · Cited by 29 · see citing works
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Hersh, W. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-094756 · Cited by 28 · see citing works
Jiang, Y., Fu, X., Wang, J., Liu, Q.-L., Wang, X., Liu, P.-J., Fu, R., Shi, J., & Wu, Y. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06530-8 · Cited by 26 · see citing works
Ray, P. P., & Majumder, P. (2023) · Journal of Clinical Neurology · doi:10.3988/jcn.2023.0158 · Cited by 23 · see citing works
Expert assessment of ChatGPT’s ability to generate illness scripts: an evaluative study
Yanagita, Y., Yokokawa, D., Fukuzawa, F., Uchida, S., Uehara, T., & Ikusaka, M. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05534-8 · Cited by 18 · see citing works
OpenAI’s ChatGPT Clinical Simulation
Musallam, E., Ali, A. A., & Alkhafaji, M. (2024) · Nurse Educator · doi:10.1097/nne.0000000000001657 · Cited by 17 · see citing works
Phanudulkitti, C., Puengrung, S., Meepong, R., Vanderboll, K., Farris, K. B., & Vordenberg, S. E. (2023) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2023.100316 · Cited by 15 · see citing works
Sarangi, P. K., Panda, B. B., Sanjay, P., Pattanayak, D., Panda, S., & Mondal, H. (2024) · Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging · doi:10.1055/s-0044-1788605 · Cited by 14 · see citing works
Shaping the future of simulator interactions: The role of ChatGPT’s Advanced Voice Mode
Barra, F. L., Costa, A., Rodella, G., Semeraro, F. F., & Carenzo, L. (2024) · Resuscitation · doi:10.1016/j.resuscitation.2024.110452 · Cited by 10 · see citing works
Mladenovic, J., van Zanten, M., & Pinsky, W. W. (2022) · Academic Medicine · doi:10.1097/acm.0000000000005051 · Cited by 7 · see citing works
Using Standardized Patients for Teaching-Learning and Assessment in a Caribbean Medical School
Shankar, P. R., & Dwivedi, N. R. (2015) · Education in Medicine Journal · doi:10.5959/eimj.v7i2.358 · Cited by 3 · see citing works
Equity-driven MCAT prep: a ChatGPT advantage
Hashemi-Sabet, F., & Leung, F. (2024) · Canadian Medical Education Journal · doi:10.36834/cmej.78169 · Cited by 3 · see citing works
Gray, M. M., Baird, A. J., Sawyer, T., et al. (2023) · doi:10.2196/preprints.50705 · Cited by 1 · see citing works
Araujo, S. M., & Cruz-Correia, R. J. (2023) · doi:10.2196/preprints.51151 · Cited by 1 · see citing works
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Kıyak, Y. S., İş-Kara, T., & Emekli, E. (2026) · Medical Science Educator · doi:10.1007/s40670-025-02621-3 · Cited by 6 · see citing works
Zouakia, Z., Logak, E., Szymczak, A., Jaïs, J., Burgun, A., & Tsopra, R. (2026) · JMIR Medical Education · doi:10.2196/82116 · Cited by 10 · see citing works
Sun, N., Zhou, X., Yang, Z., Zhou, Y., Ma, R., & Wang, Z. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1768518 · Cited by 3 · see citing works
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Thind, B. S., Javidi, D., & Schwartz, L. M. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1830254 · Cited by 2 · see citing works
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- Authors
- Cross, J., Kayalackakom, T., Robinson, R. E., Vaughans, A., Sebastian, R., Hood, R., Lewis, C., Devaraju, S., Honnavar, P., Naik, S., Joseph, J., Anand, N., Mohammed, A., Johnson, A., Cohen, E., Adeniji, T., Nnenna Nnaji, A., & George, J. E.
- Affiliations
- Medical University of the Americas (KN)
- American University of Antigua (AG)
- Northwestern University (US)
- The University of Texas Rio Grande Valley (US)
- Xavier University (US)
- Corresponding author
- Cross, Joseph
- ORCID
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2025-05-20
- Volume
- 11
- Pages
- e63353-e63353
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC12111480
- OpenAlex
- W4408559650
- Cited by
- 33
- References
- 39
- Citations by year
- 2026: 28 · 2025: 5
- FWCI
- 7.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- ChatGPT, large language models, standardized patients, virtual standardized patients, medical students, clinical interviewing skills, prompt engineering, verisimilitude, trust in AI, artificial intelligence in medical education, diagnostic skills
- MeSH terms
- Generative Artificial Intelligence, Adult, Artificial Intelligence, Education, Medical, Undergraduate, Female, Humans, Male, Middle Aged, Surveys and Questionnaires, Students, Medical, Patient Simulation, Qualitative Research, Young Adult
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