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Article: Treatment outcomes for maculopathy secondary to retinal vein occlusion in Afghanistan.

Delsoz, Mohammad / Mousavi, Sayed Hamid / Aslam, Sher A

2024 Volume 17, Issue 1, Page(s) 43–46

Abstract: Objectives: The objective of this study was to investigate the efficacy of intravitreal antivascular endothelial growth factor (VEGF) therapy in the treatment of macular edema secondary to retinal vein occlusion (RVO) in Afghanistan.: Methods: A ... ...

Abstract	Objectives: The objective of this study was to investigate the efficacy of intravitreal antivascular endothelial growth factor (VEGF) therapy in the treatment of macular edema secondary to retinal vein occlusion (RVO) in Afghanistan. Methods: A retrospective analysis was conducted of all RVO cases that underwent intravitreal ant-VEGF injection at the two leading hospitals in Kabul. The main outcome measures were visual acuity and central retinal thickness as determined by optical coherence tomography. Information was also collected on the distance traveled by each patient and the frequency of injections. Results: One hundred and twenty-five eyes of 121 patients (86 males) with RVO were identified as having undergone treatment, with a mean age of 53.1 years (range 20-80). The only agent used was bevacizumab. The mean central retinal thickness reduced from 624.2 ± 24.9 μm at the baseline to 257.8 ± 5.7 μm following treatment ( Conclusion: Despite the challenges of health-care provision in Afghanistan, this review shows that the use of intravitreal bevacizumab has provided an effective treatment for macular edema after RVO.
Language	English
Publishing date	2024-02-21
Publishing country	India
Document type	Journal Article
ZDB-ID	2484272-2
ISSN	0974-7842 ; 0974-620X
ISSN (online)	0974-7842
ISSN	0974-620X
DOI	10.4103/ojo.ojo_328_22
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Ophthalmology practice in Afghanistan during the COVID-19 pandemic.

Delsoz, M / Hollands, P

European review for medical and pharmacological sciences

2021 Volume 25, Issue 6, Page(s) 2726–2729

Abstract: This short communication described the actions taken in ophthalmic practice in Kabul, Afghanistan during the COVID-19 pandemic to effectively protect both patients and staff. By following World Health Organisation (WHO), international and local ... ...

Abstract	This short communication described the actions taken in ophthalmic practice in Kabul, Afghanistan during the COVID-19 pandemic to effectively protect both patients and staff. By following World Health Organisation (WHO), international and local guidelines it has been possible to continue treating ophthalmic outpatients with minimum risk to both patients and staff. The changes which have been implemented may allow better overall infection control in the hospital which will continue to have benefits post-pandemic.
MeSH term(s)	Afghanistan/epidemiology ; COVID-19/epidemiology ; COVID-19/transmission ; COVID-19/virology ; Eye Diseases/therapy ; Eye Diseases/virology ; Humans ; Infection Control/methods ; Infection Control/statistics & numerical data ; Ophthalmology/methods ; Ophthalmology/standards ; Personal Protective Equipment/supply & distribution ; Practice Guidelines as Topic ; SARS-CoV-2/isolation & purification
Language	English
Publishing date	2021-04-08
Publishing country	Italy
Document type	Journal Article ; Review
ZDB-ID	605550-3
ISSN	2284-0729 ; 1128-3602 ; 0392-291X
ISSN (online)	2284-0729
ISSN	1128-3602 ; 0392-291X
DOI	10.26355/eurrev_202103_25435
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Article ; Online: Applications of artificial intelligence-enabled robots and chatbots in ophthalmology: recent advances and future trends.

Madadi, Yeganeh / Delsoz, Mohammad / Khouri, Albert S / Boland, Michael / Grzybowski, Andrzej / Yousefi, Siamak

Current opinion in ophthalmology

2024 Volume 35, Issue 3, Page(s) 238–243

Abstract: Purpose of review: Recent advances in artificial intelligence (AI), robotics, and chatbots have brought these technologies to the forefront of medicine, particularly ophthalmology. These technologies have been applied in diagnosis, prognosis, surgical ... ...

Abstract	Purpose of review: Recent advances in artificial intelligence (AI), robotics, and chatbots have brought these technologies to the forefront of medicine, particularly ophthalmology. These technologies have been applied in diagnosis, prognosis, surgical operations, and patient-specific care in ophthalmology. It is thus both timely and pertinent to assess the existing landscape, recent advances, and trajectory of trends of AI, AI-enabled robots, and chatbots in ophthalmology. Recent findings: Some recent developments have integrated AI enabled robotics with diagnosis, and surgical procedures in ophthalmology. More recently, large language models (LLMs) like ChatGPT have shown promise in augmenting research capabilities and diagnosing ophthalmic diseases. These developments may portend a new era of doctor-patient-machine collaboration. Summary: Ophthalmology is undergoing a revolutionary change in research, clinical practice, and surgical interventions. Ophthalmic AI-enabled robotics and chatbot technologies based on LLMs are converging to create a new era of digital ophthalmology. Collectively, these developments portend a future in which conventional ophthalmic knowledge will be seamlessly integrated with AI to improve the patient experience and enhance therapeutic outcomes.
MeSH term(s)	Humans ; Artificial Intelligence ; Robotics ; Ophthalmology
Language	English
Publishing date	2024-01-22
Publishing country	United States
Document type	Review ; Journal Article
ZDB-ID	1049383-9
ISSN	1531-7021 ; 1040-8738
ISSN (online)	1531-7021
ISSN	1040-8738
DOI	10.1097/ICU.0000000000001035
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Article: A Response to: Letter to the Editor Regarding "The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports."

Delsoz, Mohammad / Raja, Hina / Madadi, Yeganeh / Tang, Anthony A / Wirostko, Barbara M / Kahook, Malik Y / Yousefi, Siamak

Ophthalmology and therapy

2024

Language	English
Publishing date	2024-04-18
Publishing country	England
Document type	Letter
ISSN	2193-8245
ISSN	2193-8245
DOI	10.1007/s40123-024-00937-8
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Article ; Online: Performance of ChatGPT in Diagnosis of Corneal Eye Diseases.

Delsoz, Mohammad / Madadi, Yeganeh / Raja, Hina / Munir, Wuqaas M / Tamm, Brendan / Mehravaran, Shiva / Soleimani, Mohammad / Djalilian, Ali / Yousefi, Siamak

Cornea

2024 Volume 43, Issue 5, Page(s) 664–670

Abstract: Purpose: The aim of this study was to assess the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts.: Methods: We randomly selected 20 cases of corneal diseases ... ...

Abstract	Purpose: The aim of this study was to assess the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts. Methods: We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, and degenerations from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT-3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses, compared them with the diagnoses made by 3 corneal specialists (human experts), and evaluated interobserver agreements. Results: The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct of 20 cases), whereas the accuracy of ChatGPT-3.5 was 60% (12 correct cases of 20). The accuracy of 3 corneal specialists compared with ChatGPT-4.0 and ChatGPT-3.5 was 100% (20 cases, P = 0.23, P = 0.0033), 90% (18 cases, P = 0.99, P = 0.6), and 90% (18 cases, P = 0.99, P = 0.6), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases), whereas the interobserver agreement between ChatGPT-4.0 and 3 corneal specialists was 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of 3 corneal specialists was 60% (12 cases). Conclusions: The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration. A balanced approach that combines artificial intelligence-generated insights with clinical expertise holds a key role for unveiling its full potential in eye care.
MeSH term(s)	Humans ; Artificial Intelligence ; Cornea ; Corneal Diseases/diagnosis ; Databases, Factual
Language	English
Publishing date	2024-02-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	604826-2
ISSN	1536-4798 ; 0277-3740
ISSN (online)	1536-4798
ISSN	0277-3740
DOI	10.1097/ICO.0000000000003492
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Article: Performance of ChatGPT in Diagnosis of Corneal Eye Diseases.

Delsoz, Mohammad / Madadi, Yeganeh / Munir, Wuqaas M / Tamm, Brendan / Mehravaran, Shiva / Soleimani, Mohammad / Djalilian, Ali / Yousefi, Siamak

medRxiv : the preprint server for health sciences

2023

Abstract: Introduction: Assessing the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts.: Methods: We randomly selected 20 cases of corneal diseases including corneal infections, ...

Abstract	Introduction: Assessing the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts. Methods: We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, degenerations, and injuries from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses then compared with the diagnoses of three cornea specialists (Human experts) and evaluated interobserver agreements. Results: The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct out of 20 cases) while the accuracy of ChatGPT-3.5 was 60% (12 correct cases out of 20). The accuracy of three cornea specialists were 100% (20 cases), 90% (18 cases), and 90% (18 cases), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases) while the interobserver agreement between ChatGPT-4.0 and three cornea specialists were 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of three cornea specialists was 60% (12 cases). Conclusions: The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration.
Language	English
Publishing date	2023-08-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.08.25.23294635
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Article: The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.

Delsoz, Mohammad / Raja, Hina / Madadi, Yeganeh / Tang, Anthony A / Wirostko, Barbara M / Kahook, Malik Y / Yousefi, Siamak

Ophthalmology and therapy

2023 Volume 12, Issue 6, Page(s) 3121–3132

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 ... ...

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.
Language	English
Publishing date	2023-09-14
Publishing country	England
Document type	Journal Article
ISSN	2193-8245
ISSN	2193-8245
DOI	10.1007/s40123-023-00805-x
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Article: ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports.

Madadi, Yeganeh / Delsoz, Mohammad / Lao, Priscilla A / Fong, Joseph W / Hollingsworth, T J / Kahook, Malik Y / Yousefi, Siamak

medRxiv : the preprint server for health sciences

2023

Abstract: Purpose: To evaluate the efficiency of large language models (LLMs) including ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on case reports.: Design: Prospective study.: Subjects or participants: We selected 22 different case ... ...

Abstract	Purpose: To evaluate the efficiency of large language models (LLMs) including ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on case reports. Design: Prospective study. Subjects or participants: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases that are commonly seen by neuro-ophthalmic sub-specialists. Methods: We inserted the text from each case as a new prompt into both ChatGPT v3.5 and ChatGPT Plus v4.0 and asked for the most probable diagnosis. We then presented the exact information to two neuro-ophthalmologists and recorded their diagnoses followed by comparison to responses from both versions of ChatGPT. Main outcome measures: Diagnostic accuracy in terms of number of correctly diagnosed cases among diagnoses. Results: ChatGPT v3.5, ChatGPT Plus v4.0, and the two neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreement between the various diagnostic sources were as follows: ChatGPT v3.5 and ChatGPT Plus v4.0, 13 (59%); ChatGPT v3.5 and the first neuro-ophthalmologist, 12 (55%); ChatGPT v3.5 and the second neuro-ophthalmologist, 12 (55%); ChatGPT Plus v4.0 and the first neuro-ophthalmologist, 17 (77%); ChatGPT Plus v4.0 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (17%). Conclusions: The accuracy of ChatGPT v3.5 and ChatGPT Plus v4.0 in diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. With further development, ChatGPT Plus v4.0 may have potential to be used in clinical care settings to assist clinicians in providing quick, accurate diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research.
Language	English
Publishing date	2023-09-14
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.09.13.23295508
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Article ; Online: Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study.

Raja, Hina / Munawar, Asim / Mylonas, Nikolaos / Delsoz, Mohammad / Madadi, Yeganeh / Elahi, Muhammad / Hassan, Amr / Abu Serhan, Hashem / Inam, Onur / Hernandez, Luis / Chen, Hao / Tran, Sang / Munir, Wuqaas / Abd-Alrazaq, Alaa / Yousefi, Siamak

JMIR formative research

2024 Volume 8, Page(s) e52462

Abstract: Background: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs).: Objective: The aim of this study is to evaluate the applicability of various LLMs based on textual content of ...

Abstract	Background: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs). Objective: The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers. Methods: We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease-related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field. Results: The classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F Conclusions: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.
Language	English
Publishing date	2024-03-22
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/52462
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports

Madadi, Yeganeh / Delsoz, Mohammad / Lao, Priscilla A. / Fong, Joseph W. / Hollingsworth, TJ / Kahook, Malik Y. / Yousefi, Siamak

2023

Abstract: Objective: To evaluate the efficiency of large language models (LLMs) such as ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on detailed case descriptions. Methods: We selected 22 different case reports of neuro-ophthalmic diseases from ... ...

Abstract	Objective: To evaluate the efficiency of large language models (LLMs) such as ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on detailed case descriptions. Methods: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases that are commonly seen by neuro-ophthalmic sub-specialists. We inserted the text from each case as a new prompt into both ChatGPT v3.5 and ChatGPT Plus v4.0 and asked for the most probable diagnosis. We then presented the exact information to two neuro-ophthalmologists and recorded their diagnoses followed by comparison to responses from both versions of ChatGPT. Results: ChatGPT v3.5, ChatGPT Plus v4.0, and the two neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreement between the various diagnostic sources were as follows: ChatGPT v3.5 and ChatGPT Plus v4.0, 13 (59%); ChatGPT v3.5 and the first neuro-ophthalmologist, 12 (55%); ChatGPT v3.5 and the second neuro-ophthalmologist, 12 (55%); ChatGPT Plus v4.0 and the first neuro-ophthalmologist, 17 (77%); ChatGPT Plus v4.0 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (17%). Conclusions: The accuracy of ChatGPT v3.5 and ChatGPT Plus v4.0 in diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. With further development, ChatGPT Plus v4.0 may have potential to be used in clinical care settings to assist clinicians in providing quick, accurate diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research.
Keywords	Computer Science - Computers and Society ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language
Subject code	610
Publishing date	2023-09-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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