To study functional brain abnormalities in patients with eye trauma (ET) and to discuss the pathophysiological mechanisms of ET.

Totally 31 ET patients and 31 healthy controls (HCs) were recruited. The age, gender, and educational background characteristics of the two groups were similar. After functional magnetic resonance imaging (fMRI) scanning, the subjects’ spontaneous brain activity was evaluated with the functional connectivity (FC) method. Receiver operating characteristic (ROC) curve analysis was used to classify the data. Pearson’s correlation analysis was used to explore the relationship between FC values in specific brain regions and clinical behaviors in patients with ET.

Significantly increased FC between several regions was identified including the medial prefrontal cortex (MPFC) and left hippocampus formations (HF), the MPFC and left inferior parietal lobule (IPL), the left IPL and left medial temporal lobe (MTL), the left IPL and right MTL, and the right IPL and left MTL. No decreased region-to-region connectivity was detected in default mode network (DMN) sub-regions in patients with ET. Compared with HCs, ET patients exhibited significantly increased FC between several paired DMN regions, as follows: posterior cingulate cortex (PCC) and right HF (HF.R, t=2.196, P=0.032), right inferior parietal cortices (IPC.R) and left MTL (MTL.L, t=2.243, P=0.029), and right MTL (MTL.R) and HF.R (t=2.236, P=0.029).

FC values in multiple brain regions of ET patients are abnormal, suggesting that these brain regions in ET patients may be dysfunctional, which may help to reveal the pathophysiological mechanisms of ET.

To assess the possibility of using different large language models (LLMs) in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases: ChatGPT-4, ChatGPT-3.5, Claude 2, PaLM2, and SenseNova.

A group of experienced ophthalmology professors were asked to develop a 100-question single-choice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions. The exam includes questions on the following topics: keratitis disease (20 questions), keratoconus, keratomalaciac, corneal dystrophy, corneal degeneration, erosive corneal ulcers, and corneal lesions associated with systemic diseases (20 questions), conjunctivitis disease (20 questions), trachoma, pterygoid and conjunctival tumor diseases (20 questions), and dry eye disease (20 questions). Then the total score of each LLMs and compared their mean score, mean correlation, variance, and confidence were calculated.

GPT-4 exhibited the highest performance in terms of LLMs. Comparing the average scores of the LLMs group with the four human groups, chief physician, attending physician, regular trainee, and graduate student, it was found that except for ChatGPT-4, the total score of the rest of the LLMs is lower than that of the graduate student group, which had the lowest score in the human group. Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers, giving very little chance of an incorrect answer. ChatGPT-4 showed higher credibility when answering questions, with a success rate of 59%, but gave the wrong answer to the question 28% of the time.

GPT-4 model exhibits excellent performance in both answer relevance and confidence. PaLM2 shows a positive correlation (up to 0.8) in terms of answer accuracy during the exam. In terms of answer confidence, PaLM2 is second only to GPT4 and surpasses Claude 2, SenseNova, and GPT-3.5. Despite the fact that ocular surface disease is a highly specialized discipline, GPT-4 still exhibits superior performance, suggesting that its potential and ability to be applied in this field is enormous, perhaps with the potential to be a valuable resource for medical students and clinicians in the future.