RT journal article
T1 Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review
A1 Velázquez Díaz, Daniel
A1 Arco, J.E.
A1 Ortiz, A.
A1 Pérez Cabezas, Verónica
A1 Lucena Antón, David Manuel
A1 Moral Muñoz, José Antonio
A1 Galán Mercant, Alejandro
A2 Didáctica de la Educación FísicaPlástica y Musical
A2 Enfermería y Fisioterapia
K1 Accuracy
K1 Aging
K1 Artificial intelligence
K1 Biological variability
K1 Detection
K1 Diagnosis
K1 Frail older adult
K1 Frailty
K1 Identification
K1 Older adults
K1 Review
K1 Screening
K1 Sensitivity
K1 Tool
AB BACKGROUND: Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. OBJECTIVE: The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). METHODS: A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. RESULTS: We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90. CONCLUSIONS: The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.
PB JMIR Publications Inc.
SN 1438-8871
YR 2023
FD 2023
LK http://hdl.handle.net/10498/31824
UL http://hdl.handle.net/10498/31824
LA eng
NO Spanish, Ministerio de Ciencia, Innovación y Universidades.Consejería de Economía y Conocimiento, Junta de Andalucía.
DS Repositorio Institucional de la Universidad de Cádiz
RD 12-nov-2026