Education Research: Can Large Language Models Match MS Specialist Training?
These findings indicate that while LLMs can perform at levels broadly comparable to postgraduate students, these may be particularly useful on more difficult tasks, where their consistency may complement human reasoning in a neurology subspecialty curriculum. While results should be interpreted cautiously given the limited sample size, this study illustrates possible implications of LLMs in neurology education-for example, as AI tutors for complex topics, as support for formative assessments, or as targeted review resources. Further research should assess integration into educational workflows and decision support.
Authors
- Isabella C. Wiest (ORCID: https://orcid.org/0009-0004-4848-614X)
- Hernán Inojosa (ORCID: https://orcid.org/0000-0002-1377-836X)
- Stephen Gilbert (ORCID: https://orcid.org/0000-0002-1997-1689)
- Jakob Nikolas Kather
- Tjalf Ziemssen (ORCID: https://orcid.org/0000-0001-8799-8202)
- Ahmadreza Ramezanzadeh
- Iva Gasparovic-Curtini
Institutions
- Else Kröner-Fresenius-Stiftung (DE)
- University Hospital Carl Gustav Carus (DE)
Publication Details
- Journal
- Neurology Education
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1212/ne9.0000000000200260
- Citations
- 4
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 2.62