ABSTRACT
BYOD is defined as the act of bringing your own gadget, facilities, or device to the organization or institution. The concept of BYOD has spread almost to many sectors, especially in education, due to a shortage of financial resources in the aftermath of the Novel Coronavirus 2019 pandemic. BYOD is a helpful concept in face-to-face education by giving the needy access to adequate resources. However, most of the time, students especially in higher learning institutions, are having a problem accessing adequate resources and facilities standards that may influence their productivity, performance, and perceived benefits. Moreover, the inadequacy of standardized facilities and requirements may also deprive the students of necessary productivity standards. Furthermore, the pandemic of Novel Coronavirus 2019 has transformed the current workplace practices, changing the work-life environment and warranting further exploration. Therefore, the purpose of this study is twofold;first, to identify user behavior intention to adopt BYOD, and second, to propose a conceptual model of BYOD underlying the interrelationship between BYOD antecedents and productivity. A structured literature review methodology was adopted, and a conceptual model was developed for further exploration of the topic. The contribution of this paper is as follows;first, this study identifies the antecedents of behavioral intention to adopt BYOD in the aftermath of the Novel Coronavirus 2019. Second, this study proposes a conceptual model underlying the relationship between BYOD antecedent, behavioral intention to adopt BYOD, and its impact in terms of productivity.
ABSTRACT
The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.