| Identifier |
20240305_nanos_posters_398 |
| Title |
A Comparative Study of LLMs, Human Experts, and Expert-Edited LLMs to Neuro-Ophthalmology Questions |
| Creator |
Prashant Tailor; Lauren Dalvin; Matthew Starr; Deena Tajfirouz; Kevin Chodnicki; Michael Brodsky; Sasha Mansukhani; Heather Moss; Kevin Lai; Melissa Ko; Devin Mackay; Marie DiNome; Oana Dumitrascu; Misha Pless; Eric Eggenberger; John Chen |
| Affiliation |
(PT) (LD) (MS) (DT) (KC) (MB) (SM) (MD) (OD) (MP) (EE) (JC) Mayo Clinic; (HM) Stanford School of Medicine Department of Ophthalmology, Department of Neurology & Neurological Sciences; (DM) Indiana University School of Medicine; (MK) Indiana University |
| Subject |
Miscellaneous |
| Description |
While large language models (LLMs) are increasingly used in medicine, their effectiveness compared to human experts remains unclear. This study evaluates the quality and empathy of Expert+AI, human experts, and LLM responses in neuroophthalmology. |
| Date |
2024-03 |
| References |
None provided. |
| Language |
eng |
| Format |
application/pdf |
| Type |
Text |
| Source |
2024 North American Neuro-Ophthalmology Society Annual Meeting |
| Relation is Part of |
NANOS Annual Meeting 2024: Poster Session: Analytical Studies: Neuro-Ophthalmic Practice |
| Collection |
Neuro-Ophthalmology Virtual Education Library: NANOS Annual Meeting Collection: https://novel.utah.edu/collection/nanos-annual-meeting-collection/ |
| Publisher |
North American Neuro-Ophthalmology Society |
| Holding Institution |
Spencer S. Eccles Health Sciences Library, University of Utah |
| Rights Management |
Copyright 2024. For further information regarding the rights to this collection, please visit: https://NOVEL.utah.edu/about/copyright |
| ARK |
ark:/87278/s6pf38dg |
| Setname |
ehsl_novel_nam |
| ID |
2594225 |
| Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6pf38dg |
