ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports

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

Using Large Language Models to Automate Category and Trend Analysis of Scientific Articles: An Application in Ophthalmology

Purpose: In this paper, we present an automated method for article classification, leveraging the power of Large Language Models (LLM). The primary focus is on the field of ophthalmology, but the model is extendable to other fields. Methods: We have developed a model based on Natural Language Processing (NLP) techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we have employed zero-shot learning (ZSL) LLM models and compared against Bidirectional and Auto-Regressive Transformers (BART) and its variants, and Bidirectional Encoder Representations from Transformers (BERT), and its variant such as distilBERT, SciBERT, PubmedBERT, BioBERT. Results: The classification results demonstrate the effectiveness of LLMs in categorizing large number of ophthalmology papers without human intervention. Results: To evalute the LLMs, we compiled a dataset (RenD) of 1000 ocular disease-related articles, which were expertly annotated by a panel of six specialists into 15 distinct categories. The model achieved mean accuracy of 0.86 and mean F1 of 0.85 based on the RenD dataset. Conclusion: 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 in other domains too. We performed trend analysis that enables the 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

Investigating the factors effective on the acquaintance with and use of Information and Communication Technology (ICT) in organizational responsibilities of the faculty members of the College of Agriculture and Natural Resources, University of Tehran

Nowadays it is very unlikely to come across an organization in which Information and Communication Technology is not discussed and hasn't become one of the apprehensions of the organizational managers.To insure an effective competitive edge in educational organizations, universities from all across the world must consistently improve their Information and Communication Technology.The purpose of this research was to investigate the amount of knowledge and use of Information and Communication Technology by employees. Information gathering was done by developing a questionnaire. The population of interest consisted of all the faculty members of the College of Agriculture and Natural Resources, University of Tehran, and a random group of 124 members were selected for statistical analysis. The validity of the questionnaire was approved by the statistics professionals and the foundation of it was approved by calculating the Cronbach’s alpha to 0/95.The results show that the degree of ICT use is directly and positively proportional to the educational degrees and academic ranking, and negatively proportional to work experience, and showed no correlation to age.The results of mean analysis showed a difference between the amount of usage of and familiarity with ICT between men and women and the variance analysis results showed that the amount of familiarity with and usage of ICT is different between different levels of education, academic ranks and different types of employment. Finally the results of regression analysis showed that the three variables of level of education, type of employment and age had a meaningful effect on the dependent variable of amount of familiarity with ICT, and the three variables of familiarity, type of employment and gender had a meaningful effect on the dependent variable of amount of usage of ICT. In general increasing the amount of familiarity with ICT is the most important factor effecting organization usage