Search Results (5)

Background/Objectives: Artificial intelligence (AI)-enabled digital interventions are increasingly used to expand access to mental health care. This PRISMA-ScR scoping review maps how AI technologies support mental health care across five phases: pre-treatment (screening), treatment (therapeutic support), post-treatment (monitoring), clinical education, and population-level prevention. Methods: We synthesized findings from 36 empirical studies published through January 2024 that implemented AI-driven digital tools, including large language models (LLMs), machine learning (ML) models, and conversational agents. Use cases include referral triage, remote patient monitoring, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. Results: Across the 36 included studies, the most common AI modalities included chatbots, natural language processing tools, machine learning and deep learning models, and large language model-based agents. These technologies were predominantly used for support, monitoring, and self-management purposes rather than as standalone treatments. Reported benefits included reduced wait times, increased engagement, and improved symptom tracking. However, recurring challenges such as algorithmic bias, data privacy risks, and workflow integration barriers highlight the need for ethical design and human oversight. Conclusion: By introducing a four-pillar framework, this review offers a comprehensive overview of current applications and future directions in AI-augmented mental health care. It aims to guide researchers, clinicians, and policymakers in developing safe, effective, and equitable digital mental health interventions. Full article

Voice-controlled devices are becoming increasingly common in our everyday lives as well as in medicine. Whether it is our smartphones, with voice assistants that make it easier to access functions, or IoT (Internet of Things) devices that let us control certain areas of our home with voice commands using sensors and different communication networks, or even medical robots that can be controlled by a doctor with voice instructions. Over the last decade, systems using voice control have made great progress, both in terms of accuracy of voice processing and usability. The topic of voice control is intertwined with the application of artificial intelligence (AI), as the mapping of spoken commands into written text and their understanding is mostly conducted by some kind of trained AI model. Our research had two objectives. The first was to design and develop a system that enables doctors to evaluate medical data in 3D using voice control. The second was to describe the legal and ethical issues involved in using AI-based solutions for voice control. During our research, we created a voice control module for an existing software called PathoVR, using a model taught by Google to interpret the voice commands given by the user. Our research, presented in this paper, can be divided into two parts. In the first, we have designed and developed a system that allows the user to evaluate 3D pathological medical serial sections using voice commands. In contrast, in the second part of our research, we investigated the legal and ethical issues that may arise when using voice control in the medical field. In our research, we have identified legal and ethical barriers to the use of artificial intelligence in voice control, which need to be answered in order to make this technology part of everyday medicine. Full article

Introduction: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Materials and Methods: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot’s usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot’s speech recognition. Technical shortcomings have been identified. The results of the SUS indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the “good” category. Conclusions: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features, while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance were available. Full article

In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into the field of customer relationship management (CRM), bringing benefits as well as difficulties to businesses. AI, particularly in the context of CRM, employs machine learning (ML) and deep learning (DL) techniques to extract knowledge from data, recognize trends, make decisions, and learn from mistakes with minimal human intervention. Successful firms have effectively integrated AI into CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots and virtual assistants, and voice and speech recognition. AI-driven chatbots, one of the AI-powered CRM systems, arose as a disruptive approach to customer service, and as such, unfolded with economic and managerial ramifications in CRM. Given the literature’s focus on other AI-driven systems, there is an obvious need for an investigation of industry applications and the implications of AI-driven chatbots in CRM. The purpose of this study is to explore and elucidate the economic and managerial implications of AI-powered chatbots within CRM systems. This investigation aims to provide a comprehensive understanding of how these technologies can enhance customer interactions, streamline business processes, and impact organizational strategies. To reach this goal, this study conducts a comparative qualitative analysis based on many interviews with experts and contributors in the field. Interviews with CRM specialists yielded insights into the use of AI-driven chatbots in CRM and their impact on the industry. The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance. In addition, AI chatbots have proven useful in a variety of industries, including retail and tourism. Nonetheless, there were limitations to its usage in the healthcare system, particularly in terms of ethical problems. Full article

Scientific consensus points to human activity as the primary cause of global warming triggering climate change. Mitigations include technology-assisted interventions and education of human agents, such as changing the human mindset and behavior, to avoid impending, irreversible environmental damage, among others. Among the competent sectors, religious communities can boost formative human interventions through religious ideas. The present study content analyzes the conceptual elements of “Kalikhasang Balaan” as a youth-specific ecotheological view drawn from a unidimensional Filipino measure on Christian Environmentalism. Our analysis is deductive in approach, drawing theological aspects of a peculiar youth ecotheology driven by the notion of “Kalikhasang Balaan”, framed within creation theology and Christian stewardship driven by environmental ethics. We discuss the unique attributes of this Filipino notion in the light of local Philippine church and government responses to understand how the youth ecotheological voice may correspond to institutional views. Full article