Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare systems. We analyze the balance between privacy preservation and the practical utility of healthcare data, emphasizing the effectiveness of encryption, Differential Privacy, and mixed-model approaches. The paper navigates the complex ethical and legal frameworks essential for AI integration in healthcare. We comprehensively examine patient rights and the nuances of informed consent, along with the challenges of harmonizing advanced technologies like blockchain with the General Data Protection Regulation (GDPR). The issue of algorithmic bias in healthcare is also explored, underscoring the urgent need for effective bias detection and mitigation strategies to build patient trust. The evolving roles of decentralized data sharing, regulatory frameworks, and patient agency are discussed in depth. Advocating for an interdisciplinary, multi-stakeholder approach and responsive governance, the paper aims to align healthcare AI with ethical principles, prioritize patient-centered outcomes, and steer AI towards responsible and equitable enhancements in patient care.

This systematic review emphasizes the need for technology use in older adults to reduce social isolation. With the advancement of technology over the years, the effectiveness of interventions based on its use can be examined to see how these can address the problem of social isolation and enhance social wellbeing. We focus on identifying how older adults can most benefit from affordable and accessible technology use and how the training and implementation of such interventions can be tailored to maximize their beneficial effect. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to select relevant studies. We analyzed 25 articles, performed a narrative analysis to identify themes, and quality of life indicators connected to technology use and wellbeing. Engagement of older adults at the community-level, following best practices from the Community-Based Participatory Research can facilitate effective practices to deliver technology based social isolation interventions and increase digital use self-efficacy in older adults. Mobile technology-based applications not only help families to stay connected, but also link older adults to resources in healthcare and encourage physical and mental well-being. Use of technology devices address cognitive, visual, and hearing needs, and increase digital use self-efficacy in older adults, particularly helpful during necessary social distancing or self-quarantine during the COVID-19 pandemic.

A recent MIS Quarterly article rightfully points out that service is an important part of the role of the information systems (IS) department and that most IS assessment measures have a product orientation (Pitt et al. 1995). The article went on to suggest the use of an IS-context- modified version of the SERVQUAL instrument to assess the quality of the services supplied by an information services provider (Parasuraman et al. 1985, 1988, 1991).2 However, a number of problems with the SERVQUAL instrument have been discussed in the literature (e.g., Babakus and Boiler 1992; Carman 1990; Cronin and Taylor 1992, 1994; Teas 1993). This article reviews that literature and discusses some of the implications for measuring service quality in the information systems context. Findings indicate that SERVQUAL suffers from a number of conceptual and empirical difficulties. Conceptual difficulties include the operationalization of perceived service quality as a difference or gap score, the ambiguity of the expectations construct, and the unsuitability of using a single measure of service quality across different industries. Empirical problems, which may be linked to the use of difference scores, include reduced reliability, poor convergent validity, and poor predictive validity. This suggests that (1) some alternative to difference scores is preferable and should be utilized; (2) if used, caution should be exercised in the interpretation of IS-SERVQUAL difference scores; and (3) further work is needed in the development of measures for assessing the quality of IS services.

Article discussing latent semantic analysis and five methodological recommendations