Review and future prospects of artificial intelligence in the elderly care industry

THE FUTURE PROSPECTS OF ARTIFICIAL INTELLIGENCE IN THE ELDERLY CARE INDUSTRY: A SYSTEMATIC REVIEW Your Name Subject and Section University Professor’s Name Date METHODOLOGY FOR A SYSTEMATIC REVIEW SEARCH STRATEGY The search strategy will include using various options on academic databases to identify literature for review. The databases would include PubMed, Scopus, Elsevier, ProQuest, and Google Scholar. These databases were selected based on their broad focus on medical and technology and interdisciplinary research, meaning that all the articles concerning artificial intelligence applications in elderly care would be included. KEYWORDS AND/OR PHRASES The following are the keywords and/or search terms that will be used: * Artificial intelligence or AI * Elderly care * Older adults * Health for the elderly * Aging population * Ageing * Geriatric care * Healthcare * Older adults * Robotics * Smart * Machine learning * Telemedicine or Telehealth * Quality of life or QoL * Electronic health record(s) or EHR * Electronic medical record(s) or EMR BOOLEAN OPERATORS The Boolean operators of AND, OR, and NOT will be used to broaden the search and query for the relevant literature. Conjunction AND will be employed to search for several keywords to guarantee that all these terms are harmonized in the outcomes in a bid to eliminate general literature and seek particular research that has taken a closer look at the two or more of the keywords used (for instance, "Artificial intelligence" AND "elderly care"). The OR will be applied to retrieve any of the listed keywords, thus making the search less specific and capturing any study that discusses any of the terms singly (i.e., "AI" OR "machine learning"). The NOT operator shall be employed to counterbalanced terms, which can fail to produce results pertinent to the topic under study (for example, "Robotics" NOT "surgery"). These Boolean operators will be helpful in the strategic retrieval of relevant literature, which forms this study's background. INCLUSION CRITERIA * Studies published in English. * Recent research with papers published between 2018 and 2024 will be considered. * Peer-reviewed journal articles, conference papers, and systematic reviews. * Research on adopting AI technology for the elderly in hospitals, nursing homes, home-care centers, and community-based residential care services. * Research articles elaborate on how AI enhances or has the potential to enhance the elderly's quality of life, level of independence, or health status. EXCLUSION CRITERIA * Lacking the full text of articles. * Research articles that target populations of people other than the elderly population. * Research that fails to describe the application of AI on elderly health. * Journal articles and reviews only, excluding correspondence, editorials, and commentaries DATA COLLECTION AND EXTRACTION Data collection and extraction, therefore, entails an initial review of the title and abstracts of all the papers that have been reviewed. The first step in the literature search process will involve an initial screening of the articles, which will be conducted by two independent researchers, where the candidate articles will be assessed regarding the established inclusion and exclusion criteria. It is intended to quickly exclude numerous research papers in the preliminary stage without constituting any portion of publications to be reviewed. Using two independent reviewers, we have eliminated prejudice and preconditions that would otherwise be a determinant in the screening process. After identifying articles containing the keywords in their titles, the full texts of these articles will be sought to undertake a more rigorous search. The two reviewers will do A full-text review manually without software or additional help. They will scrutinize the studies to ascertain that they meet these criteria. A consensus on the papers' suitability will be made during the peer reviewers' discussion, and if a disagreement arises, the third reviewer will be engaged. It helps to employ a methodical approach, and there is no doubt that the selection process will be constructed carefully and attentively. QUALITY ASSESSMENT The credibility of data collected from the included studies will be ensured to safeguard the research study against irretrievable loss of reliability. The studies that are incorporated into each analysis will be taken to concern their methodological quality, the quality of reporting, and relevance to a research question. The quality of the evidence which underpins the assessment will thus depend on the type of study done, the sample size, the data collection tools, and quality of analysis done. The quality of the studies will be measured by characteristics like relevance of the study type for the objectives set by the study, the coherence and practicality of the creation of the study methods, and the reliability and precision of the data provided in the study. The systematic quality assessment will help reduce bias and limitations that are inherent to the identified studies to try to work towards producing the best results. DATA SYNTHESIS As for the primary stage of data synthesis, the research will employ a qualitative approach to assess and integrate the outcomes of the incorporated studies. The second source of analysis will involve a narrative synthesis of the data collected from the literature to highlight issues of similarity and difference, as well as emerging patterns. Using this thematic analysis method will bring out the advantages, drawbacks, and possible opportunities of implementing artificial intelligence within elder care facilities. The narrative synthesis will present a summarized and categorized overview of the current state of knowledge and gaps and opportunities for further study. Besides the theoretical analysis, the qualitative synthesis of the studies describes any quantitative data presented in the analyzed papers. This will comprise identifying major statistical trends and presenting a good account supporting the provided qualitative analysis. This approach will enable a more elaborate and comprehensive review of the existing literature, which is vital for better understanding the role AI plays in assisting elderly care. REPORTING This approach is designed to follow a rigorous standard, which is adaptable for each review but focused on providing consistency and ensuring that tasks can be replicated easily. The systematic review results will be presented comprehensively and concisely. Methodological discussion and analysis will encompass the explanation of the selection of studies and the presentation of their characteristics and quality, the thematic analysis that will determine the major areas of benefits and challenges connected to the use of AI in elderly care, practical recommendations and suggestions for further research based on the synthesized bodies of evidence from the studies included in the report. POTENTIAL BARRIERS Scientific barriers are mainly ascribable to the inconsistency of the studies regarding heterogeneity. Heterogeneity in methodological frameworks, AI applications, and evaluation parameters may pose challenges in meta-analysis. Additionally, sample size, selection of participants, and short duration of follow-up are limited by design; for example, RCTs and other large-scale and longitudinal studies may impact the strength of the recommendations. Furthermore, there are some pragmatic concerns, such as the need for more time to obtain full-text articles. OUTLINE OF EXISTING KNOWLEDGE THE IMPORTANCE OF THE ELDERLY CARE Elderly care has been given much more importance today as more than half of the world's population is aging. This has been a positive transformation, yet it has posed a significant task. Higher life expectancy due to reduced prevalence of diseases and improved means of managing diseases, low birth rate, and advancement in socio-economic status is an achievement (Thinley, 2021). Although population aging has begun, the speed of this process was unknown in the past, and the number of people aged 60 years and over increased. For this reason, by 2050, two-thirds of the global population over 60 years will be in low- and middle-income countries (LMICs) (World Health Organization, 2022). A key challenge for meeting the needs of older persons is to effectively re-orient healthcare systems for older person-centered care. It also underlines the paradigm switch from the medical model of clinical practice to the bio-psychosocial model, which involves strengthening the client's resources and promoting their optimal functioning necessary to achieve a satisfactory quality of life in the elderly. Current health systems require a shift to more comprehensive care considering older persons' daily existence in their families and societies. This reorientation involves building sustainable, age-friendly health and long-term care systems involving families, communities, civil societies, and other stakeholders in the private sector (Thinley, 2021). Furthermore, various economic consequences are linked to the issue of the aging population. Prolongation of life and increased dependency characterize people's life expectancy, making the care services required for the elderly and the disabled population potential sources of jobs. However, the type of these jobs differs depending on the regions and policies (Martinez-Lacoba et al., 2021). These pose gaps in knowledge, including poor focus on aging within health strategies, limited preparedness in geriatric and gerontology studies, and limited care for carers (Thinley, 2021). These areas must be approached systematically to provide adequate, integrated systems of care that will help older people maintain their abilities and remain productive members of their households and societies. The increase in the proportion of older people requires immediate and coordinated reactions to redesign health and social services to meet older people's needs. The systematic review will amass and analyze relevant literature extolling the prospects of utilizing artificial intelligence in different aspects of elderly care to discern the benefits and drawbacks alongside the possible future developments and applications to gain a comprehensive understanding of how geriatric care may benefit from advancing technology. OPPORTUNITIES AI has great potential to provide new ways to develop elderly care, focusing more on independence among elderly people and the quality of life they enjoy. Electronic systems smart help dispense medication, monitor falls, and direct an individual so that the older persons remain as long as possible in their homes (Padhan et al., 2023). For example, AI-powered telemedicine and remote care can monitor patients' statuses, establishing trends and early signs of health deterioration without unnecessary hospitalizations and enhancing chronic disease outcomes (Ho, 2020). Moreover, diagnostics and predictive analytics, as applied to artificial intelligence, increase the effectiveness and necessary quality of medical diagnosis and prognosis with the help of big data, examining them for patterns and using prelude symptoms to detect upcoming severe health issues, which in turn can equate to better treatment and lower costs (Chen, 2020). Another vital application where AI is making an immense contribution lies around assistant devices, which include helping people perform most of their daily tasks. Robots fitted with sensors and artificial intelligence to support the elderly in mobility, hygiene, and house chores mean that the elderly have increased safety (Pradhan et al., 2023). Thus, facial robots such as humanoid robots have availed social interaction, accompanying, and therapeutic agents to elderly patients to mitigate loneliness and improve their mental health (Andtfolk et al., 2021)​. ADVANTAGES Integrating AI in elderly care has immense benefits, especially in increasing the elderly's self-reliance and well-being. People with chronic illnesses also fund artificial intelligence to monitor and facilitate daily tasks like drug administration, falling, and localization to live actively for extended periods (Padhan et al., 20...