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Delsoz M, Raja H, Madadi Y, Tang AA, Wirostko BM, Kahook MY, Yousefi S. A Response to: Letter to the Editor Regarding "The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.". Ophthalmol Ther 2024:10.1007/s40123-024-00937-8. [PMID: 38637436 DOI: 10.1007/s40123-024-00937-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/12/2024] [Indexed: 04/20/2024] Open
Affiliation(s)
- Mohammad Delsoz
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Hina Raja
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Yeganeh Madadi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Anthony A Tang
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | | | - Malik Y Kahook
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Siamak Yousefi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
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Carlà MM, Gambini G, Baldascino A, Boselli F, Giannuzzi F, Margollicci F, Rizzo S. Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison. Graefes Arch Clin Exp Ophthalmol 2024:10.1007/s00417-024-06470-5. [PMID: 38573349 DOI: 10.1007/s00417-024-06470-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
PURPOSE The aim of this study was to define the capability of ChatGPT-4 and Google Gemini in analyzing detailed glaucoma case descriptions and suggesting an accurate surgical plan. METHODS Retrospective analysis of 60 medical records of surgical glaucoma was divided into "ordinary" (n = 40) and "challenging" (n = 20) scenarios. Case descriptions were entered into ChatGPT and Bard's interfaces with the question "What kind of surgery would you perform?" and repeated three times to analyze the answers' consistency. After collecting the answers, we assessed the level of agreement with the unified opinion of three glaucoma surgeons. Moreover, we graded the quality of the responses with scores from 1 (poor quality) to 5 (excellent quality), according to the Global Quality Score (GQS) and compared the results. RESULTS ChatGPT surgical choice was consistent with those of glaucoma specialists in 35/60 cases (58%), compared to 19/60 (32%) of Gemini (p = 0.0001). Gemini was not able to complete the task in 16 cases (27%). Trabeculectomy was the most frequent choice for both chatbots (53% and 50% for ChatGPT and Gemini, respectively). In "challenging" cases, ChatGPT agreed with specialists in 9/20 choices (45%), outperforming Google Gemini performances (4/20, 20%). Overall, GQS scores were 3.5 ± 1.2 and 2.1 ± 1.5 for ChatGPT and Gemini (p = 0.002). This difference was even more marked if focusing only on "challenging" cases (1.5 ± 1.4 vs. 3.0 ± 1.5, p = 0.001). CONCLUSION ChatGPT-4 showed a good analysis performance for glaucoma surgical cases, either ordinary or challenging. On the other side, Google Gemini showed strong limitations in this setting, presenting high rates of unprecise or missed answers.
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Affiliation(s)
- Matteo Mario Carlà
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy.
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy.
| | - Gloria Gambini
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Antonio Baldascino
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Francesco Boselli
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Federico Giannuzzi
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Fabio Margollicci
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Stanislao Rizzo
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
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Delsoz M, Raja H, Madadi Y, Tang AA, Wirostko BM, Kahook MY, Yousefi S. The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports. Ophthalmol Ther 2023; 12:3121-3132. [PMID: 37707707 PMCID: PMC10640454 DOI: 10.1007/s40123-023-00805-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/29/2023] [Indexed: 09/15/2023] Open
Abstract
INTRODUCTION The purpose of this study was to evaluate the capabilities of large language models such as Chat Generative Pretrained Transformer (ChatGPT) to diagnose glaucoma based on specific clinical case descriptions with comparison to the performance of senior ophthalmology resident trainees. METHODS We selected 11 cases with primary and secondary glaucoma from a publicly accessible online database of case reports. A total of four cases had primary glaucoma including open-angle, juvenile, normal-tension, and angle-closure glaucoma, while seven cases had secondary glaucoma including pseudo-exfoliation, pigment dispersion glaucoma, glaucomatocyclitic crisis, aphakic, neovascular, aqueous misdirection, and inflammatory glaucoma. We input the text of each case detail into ChatGPT and asked for provisional and differential diagnoses. We then presented the details of 11 cases to three senior ophthalmology residents and recorded their provisional and differential diagnoses. We finally evaluated the responses based on the correct diagnoses and evaluated agreements. RESULTS The provisional diagnosis based on ChatGPT was correct in eight out of 11 (72.7%) cases and three ophthalmology residents were correct in six (54.5%), eight (72.7%), and eight (72.7%) cases, respectively. The agreement between ChatGPT and the first, second, and third ophthalmology residents were 9, 7, and 7, respectively. CONCLUSIONS The accuracy of ChatGPT in diagnosing patients with primary and secondary glaucoma, using specific case examples, was similar or better than senior ophthalmology residents. With further development, ChatGPT may have the potential to be used in clinical care settings, such as primary care offices, for triaging and in eye care clinical practices to provide objective and quick diagnoses of patients with glaucoma.
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Affiliation(s)
- Mohammad Delsoz
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Hina Raja
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Yeganeh Madadi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Anthony A Tang
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | | | - Malik Y Kahook
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Siamak Yousefi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
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