Scholarly interest in the emerging topic of multimodal learning and its ethical considerations, which integrates a variety of informational forms to enhance learning, has significantly increased during the past ten years. This paper aims to pinpoint the conceptual foundations and development trend of multimodal learning through thorough bibliometric research. By examining patterns, trends, and linkages within the published literature using a sizable collection of articles from the Scopus database, this research attempts to map the scientific landscape of multimodal learning. According to our study, multimodal learning is becoming more popular in a variety of academic disciplines. The major works and new trends in the subject were highlighted by emphasizing key publications and significant authors. While developing trends and recurrent study subjects were determined using keyword analysis, the major papers having the greatest influence on the field were found utilizing co-citation analysis. The collaborative network analysis also revealed a vibrant academic community with expanding global ties, fostering an atmosphere that is conducive to cutting-edge multimodal learning research. With its insights on the past, present, and likely future paths of multimodal learning, this study is an invaluable resource for academics, teachers, and decision-makers.

Medical image analysis has quickly advanced, with several advantages for research, diagnosis, and healthcare planning. We must have enough knowledge of current trends and developments in this field due to improvements in medical imaging technology and the accessibility of a wide range of medical image databases. This work covers the research environment for bibliometric analysis of academic articles as part of medical image analysis, utilizing the tools VOSviewer and Biblioshiny. The Scopus database contains 1,973 articles submitted between 1988 and 2023; all have been gathered and examined. The study has focused on the key bibliometric elements, such as authorship patterns, highly cited papers, prestigious journals, and collaborative networks. The outcomes of our inquiry demonstrate its interdisciplinary nature. This bibliometric analysis is a valuable resource for researchers, practitioners, and decision-makers, allowing them to identify significant trends, identify knowledge gaps, and explore prospects for further advancements in this critical field by providing a comprehensive overview of the literature on medical image analysis.

Purpose: Bibliotherapy, a therapeutic approach that uses books and reading materials to promote psychological well-being and personal growth, has become more prevalent in recent years. This scientometric study aims to provide a comprehensive view of the bibliotherapy research landscape by highlighting its evolution, trends, and noteworthy contributions using Biblioshiny and VOSviewer. Design/methodology/approach: The academic literature on bibliotherapy is evaluated in-depth in this study utilizing scientometric techniques, including citation and co-citation analysis. A thorough search of the Scopus database revealed 1,703 papers between 1942 and 2023 that dealt with bibliotherapy. For data analysis, the renowned applications Biblioshiny and VOSViewer are employed. Findings: The study reveals that the output of publications has fluctuated, reflecting scholarly interest in this discipline. The distribution of research across various countries, organizations and academic subjects is investigated further to highlight the diverse and global extent of bibliotherapy research. By analyzing co-citation networks and locating pertinent publications and authors, this scientometric method analyzes the intellectual structure of bibliotherapy research. Research limitations/implications: Bibliometric analysis enriches the theoretical understanding of bibliotherapy by unveiling the networks, influential works and existing gaps in the literature, thus guiding a more informed and collaborative approach to future research and practice in the domain. Practical implications: Employing bibliometric analysis in bibliotherapy can refine practices and training programs, ensuring they are evidence-based and practical, enhancing the quality of therapeutic services provided to individuals. Originality/value: It is a valuable resource for academics, practitioners and policymakers interested in the field since it offers a thorough and current assessment of the bibliotherapy research landscape. The findings of this study have the potential to steer future research, guide the development of bibliotherapeutic interventions supported by evidence and enhance the use of bibliotherapy as a therapeutic modality.

The place of digital twin technology in any healthcare system is a truly disruptive innovation that has profound consequences all across medical research and practice. Digital twins represent the virtual replicas that correspond to some physical entity by pulling real-time data streams from different sources to model biological systems for health monitoring and personalization of treatment strategies. This paper presents a detailed review of the current research landscape into digital twins for healthcare. Through bibliometric analysis, we obtained 1,663 publications from 2012 to 2024, basically sourced from the Scopus database, establishing a portion of the trends, productive authors, influential sources, and collaboration networks in this fast-evolving field. Descriptively, our results indicate that although research into this area started way back, the bulk of research began to be realized from 2018 onwards, with appreciable contributions coming in from interdisciplinary fields of artificial intelligence, machine learning, and data analytics. Even with challenges to data interoperability and other privacy concerns, this change brought on by digital twin technology is undoubtedly a considerable promise for chronic disease management, predictive analytics, drug discovery, and surgical planning. This work brings immense insight into this new domain of digital twins in health, which shall set up a strong foundation for future research and innovation in this area.

Widespread adoption of smart home technologies (SHTs) has changed the face of eldercare. With the help of artificial intelligence, such technologies can enable older individuals to live independently with improved security. Fall sensing, sleeping pattern monitoring, and tracking behaviors have now become features in most care settings (1). Although such technologies aim to optimize care, with them come rooted ethical concerns-one such paramount one being the erosion of privacy and dignity (2,3). As the author, I argue that technologies designed for eldercare must begin with a moral commitment to human dignity-challenging the assumption that safety and efficiency should override agency, autonomy, and emotional well-being.Older people, already vulnerable to social isolation and intellectual decline, now face an insidious threat: continuous surveillance under the guise of care. As AI technologies guess at users' behaviors and thought processes, the risk increases of the reduction of individuals to data points (4). What we are witnessing is not just technological advancement, but an ethical drift-one in which systems designed to care are beginning to control, and the goal of support is replaced by the logic of surveillance. This article argues that in developing AI to enable eldercare, we must move beyond the single-minded prioritization of protection and efficiency. This prompts a deeper question: What does it mean to age with dignity in a digitally mediated home? This question-not just technological, but profoundly human-is the compass for the reflections that follow.Dignity, difficult as it is to put into measurement, is the foundation of sound eldercare. It involves autonomy, self-respect, and the liberty to enjoy daily life without unnecessary intrusion. In the realm of AI-enhanced living, all these principles are lost (5). Though smart cameras, motion detectors, and digital aides may sense trouble sooner than human helpers, their ubiquitous presence transforms the older adult's sense of home-to no longer be sanctuaries, but surveillance zones.It is not just about surveillance. It is about the lack of symmetry in control: data is harvested, manipulated, and frequently acted on without complete knowledge or consent from the user (6). Much older adults either have no knowledge about what data is being harvested or lack the capabilities to adjust their settings as a consequence of cognitive or technical constraints (7). This generates an ethical conflict in which security is valued over agency.In addition, surveillance is psychologically harmful in the way it is likely to be experienced. Research has revealed that ongoing monitoring-even aimed at protection-actually produces feelings of anxiety, helplessness, or withdrawal from ordinary activities (8). The systems designed to add to quality of life might in fact decrease it in some instances.Beyond issues of surveillance and autonomy, AI systems in eldercare also pose distinct technical and ethical risks. Algorithmic bias, for instance, can lead to unfair treatment when models are trained on non-representative datasets-more harmful in heterogeneous aging populations. Black-box decisionmaking makes the issue worse, as the argument in favor of system-driven intervention is difficult to grasp or dispute. In high-risk applications such as fall detection or behavioral research, the lack of explainability will only lower confidence and hasten undesirable results. In addition to substantially helping in eldercare, accuracy and speed must also be transparent, interpretable, and responsible.To ground such concerns in daily life, let us turn to two real cases that bring to life the ethical challenges inherent in the use of surveillance in AI eldercare. In a pilot study in California, a fall-detection system was turned off in the home of an elderly woman after frequent false alarms triggered unannounced social service visits (9). Meant for protection, the system made her feel surveilled and powerless. Meant to support, instead, it violated her sense of agency and privacy-inseparable from the need for systems that are not only accurate but also respectful and responsive (10).In another instance, a dementia care facility deployed emotion-recognition technology to detect signs of agitation (11). While the system occasionally flagged concerning behaviors, it also generated numerous false positives. Over time, staff began to rely more on the system's cues than on their own interactions with patients (12). This shift undermined the relational, human-centered care essential for those with cognitive decline.These cases illustrate a critical point: when AI tools override human judgment or fail to account for users' comfort and consent, they risk turning care into control. Technologies must enhance, not replace, human connection-and must be designed with adaptability, transparency, and dignity at their core.To address these challenges, I propose a "Dignity-First" framework that integrates ethical principles directly into the design and deployment of AI-based eldercare systems. Informed and Ongoing Consent: Because current models of consent consistently fail, particularly in the long term with cognitively vulnerable user communities-a consent system must be regarded as an ongoing, dynamic process. In this way, user dignity and autonomy are maintained through regular check-in points and user-friendly settings that enable the user or caregiver to modify permission. With the inclusion of simple, easyto-visualize dashboards and plain-language explanations of how the data will be collected and used, such systems empower the user rather than overwhelming them.  Data Minimization and Purpose Limitation: Collecting only the data strictly necessary for specific health objectives not only protects privacy but also reduces cybersecurity vulnerabilities. Systems that avoid storing peripheral data-such as ambient audio or location history-unless clearly...