Computer Vision Syndrome in Undergraduate and Medical Students During the COVID-19 Pandemic.

Purpose: The purpose of this study was to evaluate Computer Vision Syndrome (CVS) in undergraduate and medical students since transitioning to online learning during the COVID-19 pandemic. Patients and Methods: This was a cross-sectional single center survey-based study using a validated CVS questionnaire (CVS-Q). The survey was distributed to 20,080 undergraduate students and 680 medical students at the University of Illinois at Chicago. The primary outcome measures were prevalence of CVS (based on CVS severity score of 6 or more), frequency of CVS and intensity of CVS symptoms. Results: The survey was completed by 2300 undergraduate students (11.4% response rate) and 154 medical students (22.6% response rate). The prevalence of CVS was 77.1% in undergraduate students and 69.1% in medical students. CVS-Q severity scores were highest for headaches and eye dryness, with over half of students reporting worsening of symptoms since March 2020. Increased time spent on online learning (undergraduate: P <0.001, medical: P = 0.018), blue light glasses usage (undergraduate: P <0.001, medical: P = 0.0015), and increased number of device usage were associated with higher CVS severity scores (undergraduate: P <0.001, medical: P = 0.0032). Conclusion: CVS among undergraduate and medical students has increased since the start of the COVID-19 pandemic. More focus should be placed on the management of CVS for students in higher education. Physicians should be cognizant of the consequences of online learning and be proactive about providing advice regarding preventative measures.

Optometrist's perspectives of Artificial Intelligence in eye care.

PURPOSE: The application of artificial intelligence (AI) in diagnosing and managing ocular disease has gained popularity as research highlights the utilization of AI to improve personalized medicine and healthcare outcomes. The objective of this study is to describe current optometric perspectives of AI in eye care. METHODS: Members of the American Academy of Optometry were sent an electronic invitation to complete a 17-item survey. Survey items assessed perceived advantages and concerns regarding AI using a 5-point Likert scale ranging from "strongly agree" to "strongly disagree." RESULTS: A total of 400 optometrists completed the survey. The mean number of years since optometry school completion was 25 ± 15.1. Most respondents reported familiarity with AI (66.8%). Though half of optometrists had concerns about the diagnostic accuracy of AI (53.0%), most believed it would improve the practice of optometry (72.0%). Optometrists reported their willingness to incorporate AI into practice increased from 53.3% before the COVID-19 pandemic to 65.5% after onset of the pandemic (p<0.001). CONCLUSION: In this study, optometrists are optimistic about the use of AI in eye care, and willingness to incorporate AI in clinical practice also increased after the onset of the COVID-19 pandemic.