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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

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Laili Rahayuwati

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Review Open access Aug 2026

Older adults’ participation and experiences in design thinking for AI-driven assistive technology: a scoping review

Population ageing and the rapid integration of artificial intelligence (AI) into assistive technologies have created a need to engage older adults as active partners in technology design. AI-driven assistive technologies are frequently developed with limited older-adult involvement, a pattern associated with low adoption, poor fit with daily routines, and the reinforcement of ageist assumptions about older adults as passive recipients of care. Design thinking (DT)—an empathy-driven, iterative, human-centred methodology—offers a structured framework for engaging older adults as co-designers. To date, no scoping review has mapped how older adults participate in DT processes for AI-driven assistive technology, or the ethical and experiential dimensions of that participation. This scoping review followed Arksey and O’Malley’s five-stage framework with Levac et al. refinements and was reported in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR). The protocol was prospectively registered on the Open Science Framework ( https://doi.org/10.17605/OSF.IO/8W2S5 ). Systematic searches across Web of Science, IEEE Xplore, Scopus, and PubMed/MEDLINE (10–16 March 2026; updated 20 June 2026) identified 176 records. Following deduplication (n = 146) and two-stage independent screening, 15 studies were included. Data were charted in duplicate using a piloted extraction form extended with experiential and ethical dimensions, appraised descriptively (JBI; MMAT), and synthesised through inductive thematic synthesis. The 15 included studies (2015–2025) spanned seven countries and a range of AI-driven assistive technologies, including large language model (LLM)-based conversational agents and companion robots, smart-home sensing and fall-detection systems, embodied conversational agents, and AI-enabled wearables. Thematic synthesis generated four analytical themes: (1) uneven participation across the DT cycle, concentrated in the empathise and test phases; (2) facilitators and barriers to meaningful participation; (3) a continuum of agency from tokenistic consultation to genuine co-design; and (4) structural and ethical conditions shaping inclusion. Evidence gaps were most pronounced for older adults with cognitive impairment and for older adults in low- and middle-income countries (LMICs). Meaningful participation of older adults in DT for AI-driven assistive technology is methodologically and ethically important for producing acceptable, usable, and contextually appropriate technology. This evidence map offers guidance for geriatric researchers, clinicians, digital-health implementers, and policymakers committed to inclusive co-design with older adults, and identifies priority gaps for future primary research. Open Science Framework https://doi.org/10.17605/OSF.IO/8W2S5

Dhika Dharmansyah, Laili Rahayuwati, Iqbal Pramukti et al. · 0 citations
Review Open access Aug 2026

Artificial Intelligence (AI) Implementation in Maternal and Child Health: A Scoping Review

Abstract Artificial intelligence (AI) has increasingly been applied in maternal and child health. However, current evidence remains largely focused on model development, while reports on real-world clinical implementation are limited. This scoping review aimed to map the implementation of AI in maternal and child health settings and summarize reported outcomes. This scoping review followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) guideline. Searches were conducted in PubMed and Scopus using Boolean operators combining terms related to artificial intelligence, maternal and child health, and implementation. Studies published in English between 2021 and 2026 were included if they reported clinical or community-based implementation of AI involving real patients or healthcare providers within an ongoing care pathway. Studies focused solely on AI model development or technical validation, reviews, conference abstracts, and editorials were excluded. Of 191 records identified, 156 were screened after duplicate removal, 64 full-text reports were assessed, and seven studies met all inclusion criteria. Identified AI applications were grouped into three themes: maternal support and community-based interventions, neonatal and pediatric monitoring, and screening and diagnostic support. Included studies reported promising outcomes, including improved monitoring accuracy, maternal engagement, and image quality standardization in low-resource settings. However, the seven included studies were highly heterogeneous, and most remained limited to feasibility studies or early-stage implementations. Current evidence suggests AI holds promise as an assistive tool in maternal and child healthcare. However, given the limited number and heterogeneity of included studies, this evidence should be interpreted as preliminary. Organizational, regulatory, financial, and workforce-related barriers, along with ethical considerations such as data privacy and algorithmic bias, remain to be addressed. Further large-scale, long-term implementation studies are needed to evaluate the integration and sustainability of AI in routine maternal and child healthcare practice.

Ermiati Ermiati, Laili Rahayuwati, Sheizi Prista Sari et al. · 0 citations

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