Accepted for/Published in: JMIR Aging
Date Submitted: Jul 10, 2025
Open Peer Review Period: Jul 10, 2025 - Sep 4, 2025
Date Accepted: Nov 20, 2025
(closed for review but you can still tweet)
Applications of Machine Learning for Cognitive Health in Aging Individuals with HIV: A Rapid Systematic Review
ABSTRACT
Background:
As persons with HIV (PWH) age, more than half are now over 50 years old and face an approximately 60% higher risk of developing dementia compared to the general population. In recent years, the application of artificial intelligence, particularly machine learning, combined with the growing availability of large datasets, has opened new avenues for developing prediction models to improve dementia detection, monitoring, and management.
Objective:
This systematic review aimed to synthesize the existing literature on the application of machine learning in dementia research among aging PWH and to highlight directions for future research.
Methods:
A comprehensive search was conducted in PubMed, CINAHL, and Embase in September 2024, limited to studies published in the past ten years. Eligible articles included original research involving PWH, applying at least one machine learning technique, and reporting dementia-related outcomes.
Results:
The search yielded 721 articles, of which 26 met inclusion criteria. Most studies were retrospective and conducted in the United States, focusing on neurocognitive impairment and HIV-associated neurocognitive disorders. Supervised machine learning techniques were most commonly used and demonstrated strong predictive performance. Common challenges included small sample sizes, lack of external validation, limited diversity, and concerns regarding biological interpretability and generalizability.
Conclusions:
Machine learning research on dementia in aging PWH primarily targets HIV-associated neurocognitive disorders, with limited exploration of age-related neurodegenerative diseases such as Alzheimer’s disease and related dementias. The lack of longitudinal studies and external validation remains a significant gap. This review highlights the need for future research to address all-cause dementia by expanding beyond HIV-specific conditions, employing advanced machine learning approaches and leveraging large, longitudinal, multimodal datasets. Strengthening methodological rigor and enhancing real-world clinical applications will improve early detection and effective management of cognitive health in this unique aging population. Clinical Trial: n/a
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Copyright
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