Artificial Intelligence as a Bridge to Gaps in Delirium Prediction, Detection, and Management
Year: 2026
Type: article
Abstract: BACKGROUND: Delirium affects up to one-third of hospitalized older adults and is associated with adverse outcomes. Despite its clinical importance and the availability of validated screening tools, current care gaps extend beyond under-recognition to inconsistent risk stratification, workforce constraints, documentation burden, fragmented intervention delivery, and practical challenges with monitoring of response. We review how artificial intelligence (AI) could bridge these gaps and distinguish model development and validation, real-world clinical implementation, and emerging concepts in delirium care. RESULTS: Prediction models leveraging multimodal health record data, including structured variables and unstructured clinical notes, report good discrimination for delirium risk stratification, but only a few models have been integrated into clinical practice. A live multimodal machine learning model increased targeted delirium detection, yet a pre-post comparative study found a longer length of stay; improved recognition alone therefore does not equate with improved outcomes. Natural language processing, large language models, computer vision, ambient sensing, and wearable technologies enable more continuous, objective, and less labor-intensive assessment compared with traditional screening methods, but evidence remains limited with concerns about false positives and generalizability. AI-supported delirium management through reinforcement learning and digital twins is currently largely conceptual. Responsible AI adoption requires a clearly defined intended use, external validation, action pathways, equitable performance, privacy and security, consent and transparency, clinician oversight, patient preferences, and post-deployment monitoring. CONCLUSION: AI is most defensible as an adjunct that connects evidence-based delirium care to reliable execution rather than as a replacement for clinical assessment. Prospective comparative studies should evaluate patient outcomes, resource use, and balancing measures.
DOI: 10.1111/jgs.70823
Institutions: Mayo Clinic, Aurora Health Care, The University of Texas Health Science Center at Houston, Johns Hopkins University, Johns Hopkins Medicine, Regenstrief Institute, Indiana University Indianapolis, Indiana University School of Medicine, Carolinas Medical Center, University of Cincinnati, University of Vermont
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Awards: G420310407, CTSA K12 Program at Johns Hopkins, Utilizing Technology and AI Approaches to Facilitate Independence and Resilience in Older Adults, Harnessing the Power of Technology to Transform Delirium Severity Measurement in the ICU, NIDUS II: Advanced-Stage Development and Utilization of the NIDUS Research Infrastructure to Advance Interdisciplinary Aging Research in Delirium, Penn Artificial Intelligence and Technology Collaboratory for Healthy Aging, Computational phenotyping of cancer-related cognitive impairment in older adults, Novel Continuous Predictive Wearable Agitation Monitor and Alert Using Actigraphy and AI for Hospitalized Older Adults to Prevent Delirium and Dementia for Clinical Use and Harmonization of Research, Older Americans Independence Center
OpenAlex ID: W7225330697