Related Experiment Video
Updated: Jul 13, 2026
Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
Published on: July 24, 2013
Advances of artificial intelligence in predicting frailty using real-world data: A scoping review
1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32611, United States.
Background:
Frailty assessment is imperative for tailoring healthcare interventions for older adults, but its implementation remains challenging due to the effort and time needed. The advances of artificial intelligence (AI) and natural language processing (NLP) present a novel opportunity to harness real-world data (RWD) including electronic health records, administrative claims, and other routinely collected medical records for frailty assessments.
Methods:
We followed the PRISMA-ScR guideline and searched Embase, Web of Science, and PubMed databases for articles that predict frailty using AI through RWD from inception until October 2023. We synthesized and analyzed the selected publications according to their field of application, methodologies employed, validation processes, outcomes achieved, and their respective limitations and strengths.
Results:
A total of 23 publications were selected from the initial search (N=2067) and bibliography. The approaches to frailty prediction using RWD and AI were categorized into two groups based on the type of data utilized: 1) AI models using structured data and 2) NLP techniques applied to unstructured clinical notes. We found that AI models achieved moderate to high predictive performance in predicting frailty. However, to demonstrate their clinical utility, these models require further validation using external data and a comprehensive assessment of their impact on patients' health outcomes. Additionally, the application of NLP in frailty prediction is still in its early stages. Great potential exists to enhance frailty prediction by integrating structured data and clinical notes.
Conclusion:
The combination of AI and RWD presents significant opportunities for advancing frailty assessment. To maximize the advantages of these technological advances, future research is needed to rigorously address the challenges associated with the validation of AI models and innovative data integration.
