Amidst the COVID-19 pandemic, the imperative of physical distancing has underscored the necessity for telemedicine solutions. Traditionally, telemedicine systems have operated synchronously, requiring scheduled appointments. This study introduces an innovative telemedicine system integrating Artificial Intelligence (AI) to enable asynchronous communication between physicians and patients, eliminating the need for appointments and providing round-the-clock access from any location. The AI-Telemedicine system was developed utilizing Google Sheets and Google Forms. Patients can receive dietary recommendations from the AI acting as the physician and submit self-reports through the system. Physicians have access to patients’ submitted reports and can adjust AI settings to tailor recommendations accordingly. The AI-Telemedicine system for patients requiring daily dietary recommendations has been successfully developed, meeting all nine system requirements. System privacy and security are ensured through user account access controls within Google Sheets. This AI-Telemedicine system facilitates seamless communication between physicians and patients in situations requiring physical distancing, eliminating the need for appointments. Patients have round-the-clock access to the system, with AI serving as a physician surrogate whenever necessary. This system serves as a potential model for future telemedicine solutions.

The use of artificial intelligence (AI) is related to the dynamic development of digital skills. This article focuses on the impact of AI on the work of non-profit organizations that aim to help those around them. Based on 10 semi-structured interviews, it is presented here how it is possible to work with AI and in which areas it can be used—in social marketing, project management, routine bureaucracy. At the same time, workers and volunteers need to be educated in critical and logical thinking more than ever before. These days, AI is becoming more and more present in almost all the activities, bringing several benefits to those making use of it. On the one hand, by using AI in the day-to-day activities, the entities are able to substantially decrease their costs and have the advantage of being able to have, in most cases, a better and faster job done. On the other hand, those individuals that are more creative and more innovative in their line of work should not feel threatened by those situations in which organizations decide to use more AI technologies rather than human beings for the routine activities, since they will get the opportunity to perform tasks that truly require their intellectual capital and decision making abilities.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.

The purpose of the study was to examine the role of personalization in motivating senior citizens to use AI driven fitness apps. Vroom’s expectancy theory of motivation was applied to examine the motivation of senior citizens. The responses from participants were collected through structured interviews. The participants belonged to South Asian origin belonging to India, Bangladesh, Nepal and Bhutan. The authors adopted a content analysis approach where the gathered interview responses were coded in the context of elements of Vroom’s theory. The findings of the study indicated that a highly personalized approach in the context of motivation, expectancy, instrumentality and valence will motivate senior citizens to use AI based fitness apps. The study contributes to the personalization of AI fitness apps for senior citizens.