AI-Driven Smart Elderly Care: Innovations and Solutions

Topic Information

Dear Colleagues,

This topic focuses on emerging artificial intelligence (AI) technologies and their transformative potential in the domain of smart elderly care. With the global ageing population projected to double by 2050, there is a pressing need to design and implement intelligent systems that can support independent living, remote monitoring, and predictive health management for older adults. The integration of AI with Internet of Things (IoT), edge computing, robotics, and wearable technologies has enabled the development of smart environments that can assist elderly individuals in maintaining autonomy, improving safety, and enhancing quality of life.

This topic aims to explore the latest innovations, practical deployments, and theoretical advancements in AI-enabled elderly care systems, including personalised assistive technologies, emotion-aware computing, ethical and privacy-preserving frameworks, and data-driven decision support models.

Topics of interest include, but are not limited to the following:

• AI-driven predictive health analytics and anomaly detection;
• IoT-enabled ambient assisted living (AAL) systems;
• AI and robotics in elderly rehabilitation and surgical assistance;
• Telemedicine and virtual healthcare platforms for ageing populations;
• Privacy-preserving and secure AI models in elderly care environments;
• Generative AI and its application in mental health support for seniors;
• Emotion-aware computing and socially assistive AI companions;
• Wearable sensing technologies integrated with machine learning models;
• Natural language processing (NLP) interfaces for elderly engagement;
• Ethical, regulatory, and policy considerations in AI-based gerontology.

We encourage submissions from multiple disciplinary lenses, including healthcare informatics, human-centred computing, AI ethics, clinical engineering, cybersecurity, and digital health. We welcome original research articles, reviews, technical developments, and application case studies that advance the state of knowledge in this crucial area of intelligent systems for elderly care.

Dr. Mahmoud Elkhodr
Prof. Dr. Ergun Gide
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
Geriatrics geriatrics	2.1	3.4	2016	19.5 Days	CHF 1800	Submit
Journal of Ageing and Longevity jal	-	-	2021	23.3 Days	CHF 1000	Submit
Healthcare healthcare	2.7	4.7	2013	21.5 Days	CHF 2700	Submit

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Published Papers (2 papers)

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All AI Geriatrics JAL Healthcare

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Open AccessArticle

An Artificial Intelligence-Assisted Smartphone Application for Improving Dietary Quality Among Frail Older Adults: A Quasi-Experimental Study

by Kayo Kurotani, Hikaru Tanabe, Keiji Yanai, Kazunori Sakamoto and Kazunori Ohkawara

Geriatrics 2025, 10(6), 160; https://doi.org/10.3390/geriatrics10060160 - 4 Dec 2025

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Abstract

Background/Objectives: Although information and communication technology (ICT) offers opportunities to address challenges, evidence among frail populations is limited. We aimed to evaluate the effectiveness and feasibility of an ICT-based intervention incorporating an artificial intelligence (AI)-assisted smartphone dietary application and group communication tools [...] Read more.

Background/Objectives: Although information and communication technology (ICT) offers opportunities to address challenges, evidence among frail populations is limited. We aimed to evaluate the effectiveness and feasibility of an ICT-based intervention incorporating an artificial intelligence (AI)-assisted smartphone dietary application and group communication tools to improve dietary quality and social connection among community-dwelling older adults with frailty. Methods: A non-randomized, quasi-experimental study was conducted among 29 older adults (≥65 years) in Tokyo, Japan. Participants were assigned to the intervention (n = 11) or control (n = 18) group. The 3-month intervention included weekly photo uploads of meals via an AI-based dietary application providing automated image analysis and personalized feedback, supervised by registered dietitians, along with peer communication through a group chat. The primary outcome was dietary quality. The secondary outcomes included body weight, body mass index (BMI), skin carotenoid score, and loneliness. Results: The adjusted Japanese Food Guide Spinning Top Score at 3-month follow-up was 49.0 (standard error [SE] = 2.6) and 39.5 (SE = 2.0) in the intervention and control groups, respectively. The adjusted mean difference between groups was +9.5 (95% confidence interval: 2.3 to 16.7, p = 0.01). After using analysis of covariance for adjusting for respective baseline values, age, education status, and antihypertension drug use, no statistically significant between-group differences were observed at 3-month follow-up for any secondary outcomes. Conclusions: AI-based dietary intervention and peer communication effectively improved dietary quality among older adults, highlighting the potential of such an intervention to promote healthier eating habits in this population. Full article

(This article belongs to the Topic AI-Driven Smart Elderly Care: Innovations and Solutions)

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17 pages, 1888 KB  

Open AccessSystematic Review

Comparing the Effects of AI-Assisted and Traditional Exercise on Physical Health Outcomes in Older Adults: A Systematic Review and Meta-Analysis

by Sijing Fan, Xin Tan, Hongyun Zheng, Yicong Cui, Xiaotong Du, Boqiao Huang, Jingzhan Ren, Xinming Ye and Wen Fang

Healthcare 2025, 13(23), 2999; https://doi.org/10.3390/healthcare13232999 - 21 Nov 2025

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Abstract

Objective: Exercise is widely recognized as an effective non-pharmacological intervention to maintain health in older adults. With advances in artificial intelligence (AI), AI-assisted exercise has emerged as a novel rehabilitation approach, yet its comparative effectiveness against traditional and software-assisted programs remains unclear. This [...] Read more.

Objective: Exercise is widely recognized as an effective non-pharmacological intervention to maintain health in older adults. With advances in artificial intelligence (AI), AI-assisted exercise has emerged as a novel rehabilitation approach, yet its comparative effectiveness against traditional and software-assisted programs remains unclear. This study aimed to evaluate and rank the relative effectiveness of these interventions on multiple physical and psychological outcomes using a network meta-analysis (NMA). Methods: Following the PRISMA-NMA guidelines, we systematically searched PubMed, Embase, Cochrane Library, Web of Science, and Scopus up to June 2025. Eligible studies were randomized controlled trials (RCTs) involving adults ≥ 60 years comparing AI-assisted, software-assisted, and conventional upper/lower limb rehabilitation. Six outcomes were analyzed: gait, balance, range of motion (ROM), muscle strength, cognitive function, and quality of life (QOL). Stata 17.0 was used to conduct the NMA, calculating the standardized mean differences (SMDs) and SUCRA rankings, with assessments of heterogeneity and risk of bias. Results: Seventy RCTs with 808 participants were included. All active interventions outperformed the placebo. AI-assisted programs showed the strongest effects on gait (SMD = 1.33) and balance (SMD = 0.76), while software-assisted interventions ranked highest for ROM (SMD = 0.69) and QOL (SMD = 1.06). Both AI and software interventions improved cognition and muscle strength. Heterogeneity was low (I² ≤ 38.5%). Subgroup analysis indicated that AI-based methods were superior to traditional rehabilitation, although differences among novel interventions were not statistically significant. Conclusions: AI-assisted exercise is highly effective for gait and balance, while software-assisted approaches excel in ROM and QOL. These interventions hold promise for community and home-based rehabilitation. Future studies should investigate integrated “AI + traditional” models and incorporate biomechanical and neurophysiological indicators to optimize personalized care. Full article

(This article belongs to the Topic AI-Driven Smart Elderly Care: Innovations and Solutions)

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