Representation of Rural Older Adults in AI for Health Research: Systematic Literature Review
Notice bibliographique
Résumé
Background: Older adults in rural communities face unique challenges, including geographic isolation, a shortage of health care providers, and limited access to specialized services. Artificial intelligence (AI) has emerged as a promising solution for improving health care access and delivery. However, concerns persist about equitable access and representation in these innovations, especially for marginalized populations where technological literacy and infrastructure may present additional barriers to effective use. Objective: This systematic literature review aims to: (1) identify existing literature on AI for health research focused on rural older adults, and (2) critically evaluate the identified literature to highlight gaps and inform the development of inclusive AI for health research and design practices. Methods: Between January 2024 and March 2025, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Protocol to conduct a systematic search of health and computer science literature. We searched 7 databases (PubMed, CINAHL Plus with Full Text, PsycINFO, Web of Science, IEEE Explore, ACM Digital Library, and Scopus) to identify relevant research papers published between January 2013 and December 2023. We used predetermined search terms and built-in result limiters, including English language, human subjects, full text, and aged 65+. Publications were excluded if they were not empirical or did not include a focus on older adults, rural populations, or AI. The resulting data were reviewed, coded, and analyzed using thematic analysis. Results: A total of 23 papers comprised the final sample. The results showed that the representation of rural older adults in AI for health literature is limited. We identified three salient themes: (1) Numbers over Narratives: The Quantitative Focus in AI for Health Research on Older Adults, (2) Efficacy over Impact: Prevalence Clinical Outcomes in AI for Health Research, and (3) Deepening Disparities: Representation of Rurality Missing in AI for Health Research. These themes underscore the need for a more nuanced understanding of how AI for health research can be tailored to the specific needs of rural older populations. Conclusions: Our systematic analysis identified a robust body of research on AI for older adults. However, a critical gap emerged with a dearth of studies explicitly focusing on older adults in rural communities. This lack of representation raises concerns about the generalizability of findings and the potential for exacerbating existing health care disparities in rural areas. Future research should: (1) prioritize targeted recruitment strategies for rural older adult participants to ensure better representation in AI for health research; (2) develop community-based AI policies, practices, and products that reflect the specific needs and contexts of rural populations; and (3) explore solutions that address the limited representation of rural communities, ensuring that AI interventions are equitable, accessible, and beneficial for all.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,040 | 0,179 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,008 |
| Bibliométrie | 0,021 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».