Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.

Recent research into user experience has identified the need for a theoretical model to build cumulative knowledge in research addressing how the overall quality or 'goodness' of an interactive product is formed. An experiment tested and extended Hassenzahl's model of aesthetic experience. The study used a 2 × 2 × (2) experimental design with three factors: principles of screen design, principles for organizing information on a web page and experience of using a web site. Dependent variables included hedonic perceptions and evaluations of a web site as well as measures of task performance, navigation behaviour and mental effort. Measures, except Beauty, were sensitive to manipulation of web design. Beauty was influenced by hedonic attributes (identification and stimulation), but Goodness by both hedonic and pragmatic (user-perceived usability) attributes as well as task performance and mental effort. Hedonic quality was more stable with experience of web-site use than pragmatic quality and Beauty was more stable than Goodness