Large language models (LLM) and ChatGPT: a medical student perspective
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These studies involved faculty and mostly medical students, but also other health-related students from countries worldwide, including Europe, the Middle East, northern and sub-Saharan Africa, North America, Asia, Australia, and New Zealand.
“These studies involved faculty and mostly medical students, but also other health-related students from countries worldwide, including Europe, the Middle East, northern and sub-Saharan Africa, North America, Asia, Australia, and New Zealand [24–29].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [29]
Eight studies examined the knowledge, attitudes, and skills of medical students and educators regarding LLMs, specifically ChatGPT; students generally expressed a more positive attitude toward LLMs than practicing physicians; those more knowledgeable about LLMs tended to hold a more positive attitude and were more likely to use them for educational purposes; and concerns were raised regarding the
“Eight studies examined the knowledge, attitudes, and skills of medical students and educators regarding LLMs, specifically ChatGPT [24, 26–29, 79–81]. Students generally expressed a more positive attitude toward LLMs than practicing physicians, showing optimism about their potential to support learners and educators [81]. Those who were more knowledgeable about LLMs tended to hold a more positive”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [29]
Cites
Will Artificial Intelligence Replace Radiologists?
Langlotz, C. P. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019190058 · Cited by 254 · see citing works
Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be?
Alberts, I. L., Mercolli, L., Pyka, T., Prenosil, G., Shi, K., Rominger, A., & Afshar-Oromieh, A. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06172-w · Cited by 207 · see citing works
Integrating artificial intelligence into radiology practice: undergraduate students’ perspective
Arachchige, A. S. P. M., & Svet, A. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05558-y · Cited by 14 · see citing works
Flagged first, then by citations
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Impact of AI assistance on student agency
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024) · Computers & Education · doi:10.1016/j.compedu.2023.104967 · Cited by 548 · see citing works
“…ways, including generating text for assignments and exams, answering questions, and providing teaching support; however, they have also raised critical questions about their integration into teaching and assessment practices (Yan et al., 2023; Alqahtani et al., 2023; Arachchige & Arosh, 2023).”
BackgroundThe application of large language models in medicine: A scoping review
Meng, X., Yan, X., Zhang, K., Liu, D., Cui, X., Yang, Y., Zhang, M., Cao, C., Wang, J., Wang, X., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109713 · Cited by 414 · see citing works
A systematic review of large language models and their implications in medical education
Lucas, H. C., Upperman, J. S., & Robinson, J. R. (2024) · Medical Education · doi:10.1111/medu.15402 · Cited by 287 · see citing works
Xu, X., Chen, Y., & Miao, J. (2024) · Journal of Educational Evaluation for Health Professions · doi:10.3352/jeehp.2024.21.6 · Cited by 169 · see citing works
A scoping review of ChatGPT's role in healthcare education and research
Shorey, S., Mattar, C., Pereira, T. L.-B., & Choolani, M. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106121 · Cited by 96 · 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
Artificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62 · see citing works
Assertion detection in clinical natural language processing using large language models
Ji, Y., Yu, Z., & Wang, Y. (2024) · 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00039 · Cited by 14 · see citing works
- Authors
- Perera Molligoda Arachchige, A. S.
- Venue
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2023-04-13
- Volume
- 50
- Issue
- 8
- Pages
- 2248-2249
- ISSN
- 1619-7070 (ISSN-L)
- 1619-7089
- OpenAlex
- W4365443059
- Cited by
- 35
- References
- 3
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 16 · 2023: 6
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- European Journal of Nuclear Medicine and Molecular Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- large language models, ChatGPT, medical students, artificial intelligence in healthcare, medical education
- MeSH terms
- Artificial Intelligence, Language
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