Background: Intrinsic capacity (IC) has become a central concept in healthy aging because it emphasizes functional ability across the aging trajectory rather than disease alone. However, current IC assessment primarily relies on episodic clinical evaluations, which are insufficient for continuous monitoring and early identification of functional decline. Recent advances in AI and digital health technologies have created new opportunities for objective, continuous, and real-world assessment of IC. However, existing evidence remains fragmented across AI-enabled devices, digital biomarkers (DBs), AI techniques, and IC domains. Objective: This scoping review aimed to systematically synthesize the current evidence on AI-based measurement tools for IC and to characterize the landscape of AI-enabled IC assessment using a 3D analytical framework integrating AI-enabled digital devices and systems, DBs, and AI techniques. Methods: A comprehensive search of PubMed, Embase, CINAHL, PsycINFO, the Cochrane Library, SinoMed, and China National Knowledge Infrastructure (CNKI) was conducted from database inception to July 2025 and updated on May 31, 2026, in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline. Studies investigating AI-based measurement tools applicable to one or more IC domains were included. Results: A total of 161 studies met the inclusion criteria. Research on AI-based measurement tools for IC has expanded rapidly since 2016, with studies conducted in 28 countries, predominantly the United States and China. Most studies focused on a single IC domain, with cognition accounting for the largest proportion. Eleven categories of AI-enabled digital devices and systems were identified, among which multimodal data acquisition devices, computer vision (CV) systems, and AI-driven health platforms were the most frequently reported. Twenty-one types of DBs were extracted and classified into 3 major categories, with gait parameters, digital task performance, physical activity features, speech and language features, and facial features representing the most commonly used biomarkers. Machine learning and deep learning were the predominant AI techniques, while CV and natural language processing played central roles in multimodal data interpretation. The distribution and maturity of evidence varied substantially across domains, with cognition and locomotor capacity representing the most developed areas, whereas vitality, hearing, and multidomain IC assessment remained comparatively underrepresented. Conclusions: This scoping review provides a 3D synthesis of AI-enabled digital devices and systems, DBs, and AI techniques across the 6 World Health Organization (WHO)–defined domains of IC. Unlike previous technology-, disease-, or domain-specific reviews, it compares evidence across the broader IC framework, identifying more developed areas, key evidence gaps, and priorities for standardization, external validation, and multidomain assessment. AI-based measurement tools may complement conventional assessment in community, primary care, and home settings, although their clinical translation will require robust validation, integration into care pathways, and implementation approaches that address the needs of older adults.

Background: Intrinsic capacity (IC) is key to promoting healthy aging, and managing declines in IC is crucial for delaying functional deterioration in older adults. Digital health interventions (DHIs) hold promising potential for addressing IC decline. This scoping review aims to synthesize existing evidence by mapping the types of DHIs employed and examining their effects across the five domains of IC in older adults. Methods: The review was conducted following the five-stage framework of Arksey and O’Malley and the PRISMA-ScR guideline. The search was performed across PubMed, Embase, CINAHL, Cochrane Library, PsycINFO, SinoMed, and CNKI databases for studies published between 1 January 2015 and 31 July 2025. Relevant studies were identified using MeSH terms and free-text terms related to “older adults”, “digital health”, and “intrinsic capacity”. Results: Based on the eligibility criteria, 81 studies were included. The DHIs identified encompassed virtual reality, exergames, computerized cognitive training, mHealth, internet-based interventions, telehealth, digital hearing aids, assistive robotics, and visual biofeedback. Most studies focused on single-domain interventions (74%), with cognition being the most targeted (40.7%), while sensory (4.9%) and vitality (2.5%) domains received the least attention. No digital interventions targeted all five IC domains. Regarding efficacy, many DHIs reported statistically significant improvements in one or more IC domains; however, the magnitude and consistency of these effects varied considerably across studies. Conclusions: Preliminary evidence suggests that DHIs show potential in managing declines in IC among older adults. However, evidence quality varies significantly, often derived from small-scale studies. Future research should focus on establishing clinical effectiveness through adequately powered trials and on integrating DHIs into comprehensive intervention strategies that target all domains of IC, with robust evaluation of their outcomes.