Transforming Medical Communication Education: The Effectiveness of Generative AI Education Using ChatGPT
Open accessAbstract
Objectives: This study aims to explore the effectiveness of medical communication skills training through generative AI (Artificial Intelligence), such as ChatGPT. The primary objective is to analyze the impact of AI-based education on students’ communication abilities and learning experiences, while also discussing the long-term potential of integrating AI into medical education. Methods: A communication education utilizing Generative AI was designed for 70 first-year medical students. The program employed a multifaceted approach, including virtual patient simulations, real-time feedback, and selfreflection. Post-training surveys were conducted to assess student satisfaction and learning outcomes. Both quantitative and qualitative data were analyzed to derive the results.Results: Students perceived AI-based training as having a positive impact on their communication skills. In particular, realtime feedback and the opportunity for repeated practice contributed to boosting students’ confidence. The average satisfaction score exceeded 3.7, with students responding favorably to the interaction with AI, believing it would enhance their communication skills in real clinical settings. Conclusions: This study demonstrates the potential of generative AI as a valuable tool in medical education, contributing to the improvement of communication training quality. Future research should focus on expanding the scope of AI applications and analyzing the long-term effectiveness of such training programs across a wider range of clinical scenarios.
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Cites
The Effects of Physician Empathy on Patient Satisfaction and Compliance
Kim, S. S., Kaplowitz, S., & Johnston, M. V. (2004) · Evaluation & the Health Professions · doi:10.1177/0163278704267037 · Cited by 1,063 · see citing works
Eysenbach, G. (2023) · JMIR Medical Education · doi:10.2196/46885 · Cited by 840 · see citing works
Assessing Competence in Communication and Interpersonal Skills: The Kalamazoo II Report
Duffy, F. D., Gordon, G. H., Whelan, G. P., Cole‐Kelly, K., & Frankel, R. M. (2004) · Academic Medicine · doi:10.1097/00001888-200406000-00002 · Cited by 664 · see citing works
The rise of <scp>ChatGPT</scp> : Exploring its potential in medical education
Lee, H. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2270 · Cited by 596 · see citing works
Artificial intelligence in medical education
Masters, K. (2019) · Medical Teacher · doi:10.1080/0142159x.2019.1595557 · Cited by 469 · see citing works
A systematic review of the associations between empathy measures and patient outcomes in cancer care
Lelorain, S., Brédart, A., Dolbeault, S., & Sultan, S. (2012) · Psycho-Oncology · doi:10.1002/pon.2115 · Cited by 291 · see citing works
Lorenzini, G., Ossa, L. A., Shaw, D., & Elger, B. S. (2023) · Bioethics · doi:10.1111/bioe.13158 · Cited by 167 · see citing works
Using artificial intelligence to analyse and teach communication in healthcare
Butow, P., & Hoque, E. (2020) · The Breast · doi:10.1016/j.breast.2020.01.008 · Cited by 118 · see citing works
Consultation skills of young doctors: II--Most young doctors are bad at giving information.
Maguire, P., Fairbairn, S., & Fletcher, C. (1986) · BMJ · doi:10.1136/bmj.292.6535.1576 · Cited by 88 · see citing works
Using artificial intelligence to assess clinicians’ communication skills
Ryan, P., Luz, S., Albert, P., Vogel, C., Normand, C., & Elwyn, G. (2019) · BMJ · doi:10.1136/bmj.l161 · Cited by 69 · see citing works
Lee, Y.-M., Kim, S., Lee, Y.-H., Kim, H.-S., Seo, S. W., Kim, H., & Kim, K. J. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005618 · Cited by 60 · see citing works
Medical students’ communication skills in clinical education: Results from a cohort study
Bachmann, C., Roschlaub, S., Harendza, S., Keim, R., & Scherer, M. (2017) · Patient Education and Counseling · doi:10.1016/j.pec.2017.05.030 · Cited by 41 · see citing works
Bowers, P. J., Graydon, K., Ryan, T., Lau, J. H., & Tomlin, D. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.9307 · Cited by 22 · see citing works
Findley, J. C., Schatte, D. J., & Power, J. (2017) · MedEdPORTAL · doi:10.15766/mep_2374-8265.10612 · Cited by 14 · see citing works
Kushwaha, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4872298 · Cited by 6 · see citing works
Chu, S. N., & Goodell, A. J. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.19941 · Cited by 6 · see citing works
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Research works printed but not linked: 1
- Eysenbach G. The role of ChatGPT, generative language models, and artificial intelligence in medical education: A conversation with Chat- GPT and a call for papers. JMIR Med Educ 2023;9:e46885. DOI: 10. 2196/46885 · DOI · Paper page
Cited by
Sorted by citations
Ebihara, H., Kasai, H., Shimizu, I., Shikino, K., Tajima, H., Kimura, Y., & Ito, S. (2025) · JMIR Medical Education · doi:10.2196/76702 · Cited by 10 · see citing works
“Furthermore, gAI has been recognized as a useful tool for retrieving information, enhancing students’ communication techniques, and providing general learning support [31,32].”
Choose Your Own Adventure: Using Twine for Gamified Interactive Learning in Veterinary Anaesthesia
Redondo, J. I., Marti‐Scharfhausen, R., Martínez-Albiñana, A., Cañón, A., Gutiérrez-Bautista, Á. J., Viscasillas, J., & Hernandez-Magaña, E. Z. (2025) · Veterinary Sciences · doi:10.3390/vetsci12020156 · Cited by 2 · see citing works
Khojasteh, L., Nasiri, E., & Moslehi, S. (2025) · Research Square · doi:10.21203/rs.3.rs-7829698/v1
Wang, D., Liang, K., Zheng, Q., Song, W., Whiting, J., Yao, M. Z., & Cao, C. G. L. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791909
- Authors
- Ji, Y.-A., Kim, G., & Seo, J.-H.
- Affiliations
- Gyeongsang National University (KR)
- Venue
- Journal of Health Informatics and Statistics
- Published
- 2024-11-30
- Volume
- 49
- Issue
- 4
- Pages
- 332-338
- ISSN
- 2465-8014 (ISSN-L)
- 2465-8022
- OpenAlex
- W4405154997
- Cited by
- 4
- References
- 13
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Health Informatics and Statistics (publishedVersion)
- Funders
- Gyeongsang National University
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- medical communication skills, generative AI, ChatGPT, medical students, virtual patient simulation, real-time feedback, self-reflection, student satisfaction, clinical communication training
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