Technology and Ethics: ChatGPT in Neuro-Ophthalmology
| Identifier | 20240304_nanos_hottopics1_03 |
| Title | Technology and Ethics: ChatGPT in Neuro-Ophthalmology |
| Creator | Heather E. Moss, MD, PhD |
| Affiliation | Stanford University, Palo Alto, CA |
| Subject | Large Language Models (LLMS); Medical Ethics; Answer Questions; Summarize or Edit Text |
| Description | Our personal and professional lives are replete with models that classify (e.g. lab test norms, OCT segmentation) and predict (auto-text complete). In recent NANOS meetings we have heard presentations on supervised (i.e. programmer defines the categories) and unsupervised (i.e. model figures out the categories) machine learning models which have a promise of supporting complex clinical classification tasks. Generative artificial intelligence (AI), for example the large language model GPT3.5 and the chatbot it powers (ChatGPT), take a leap forward beyond classification and prediction to generate new content. |
| Date | 2024-03 |
| References | 1. MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN NEURO-OPHTHALMOLOGY 2. Doshi H, Solli E, Elze T, Pasquale LR, Wall M, Kupersmith MJ. Unsupervised Machine Learning Shows Change in Visual Field Loss in the Idiopathic Intracranial Hypertension Treatment Trial. Ophthalmology. 2022 Aug;129(8):903-911. 3. Dumitrascu OM, Wang Y, Chen JJ. Clinical Machine Learning Modeling Studies: Methodology and Data Reporting. J Neuroophthalmol. 2022 Jun 1;42(2):145-148. 4. Milea D, Najjar RP, Zhubo J, Ting D, Vasseneix C, Xu X, Aghsaei Fard M, Fonseca P, Vanikieti K, Lagrèze WA, La Morgia C, Cheung CY, Hamann S, Chiquet C, Sanda N, Yang H, Mejico LJ, Rougier MB, Kho R, Thi Ha Chau T, Singhal S, Gohier P, Clermont-Vignal C, Cheng C-Y, Jonas JB, Yu-Wai-Man P, Fraser CL, Chen JJ, Ambika S, Miller NR, Liu Y, Newman NJ, Wong TY, Biousse V; BONSAI Group. Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs. N Engl J Med. 2020 Apr 30;382(18):1687-1695 5. Zipori AB, Kerley CI, Klein A, Kenney RC. Real-World Translation of Artificial Intelligence in Neuro-Ophthalmology: The Challenges of Making an Artificial Intelligence System Applicable to Clinical Practice. J Neuroophthalmol. 2022 Sep 1;42(3):287-291. |
| Language | eng |
| Format | video/mp4 |
| Type | Image/MovingImage |
| Source | 2024 North American Neuro-Ophthalmology Society Annual Meeting |
| Relation is Part of | NANOS Annual Meeting 2024: Hot Topics |
| 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/s65kw2jf |
| Setname | ehsl_novel_nam |
| ID | 2589933 |
| Reference URL | https://collections.lib.utah.edu/ark:/87278/s65kw2jf |