EXPLORING AI FOR DEMENTIA CARE SUPPORT IN NORTHERN ONTARIO: A LITERATURE REVIEW
Bibliographic record
Abstract
Abstract Dementia is the seventh leading cause of death globally, significantly impairs cognitive abilities and memory loss. Artificial intelligence (AI) has the potential to revolutionize dementia care by assisting healthcare professionals in diagnosing, managing, and treating dementia worldwide. We conducted literature review using Google Scholar, PubMed, and Laurentian libraries (OMNI), focusing on key terms such as “dementia,” “Alzheimer’s disease,” “artificial intelligence,” and “healthcare.” This review covered peer-reviewed publications from 2014 to 2023, aiming to fill gaps in this research area. The search yielded five relevant articles, identifying three core themes: AI’s impact on Alzheimer’s disease and related dementias (ADRD), early detection and diagnosis of ADRD, and AI’s role in supporting healthcare professionals, caregivers, and patients. Zhang et al. (2023) emphasized AI’s role in diagnosing Alzheimer’s disease using MRI, PET scans, biomarkers, and advanced machine learning applications. Hane et al. (2020) highlighted the use of machine learning models for assessing ADRD risk by analyzing clinical notes to prevent nursing home admissions and reduce care costs. Xie et al. (2020) explored the use of assistive AI devices by caregivers to manage ADRD, reducing patient dependence. Li et al. (2020) focused on voice-activated AI tools that help caregivers manage nutrition and reduce their workload. Gustavsson, Svanberg, and Mullersdorf (2015) examined the benefits of the interactive robotic “Justo Cat” in enhancing communication in dementia care. This review suggests conducting ethnographic participatory focused-groups with thematic analysis to inform policymakers in developing specialized educational programs, integrating advanced AI, expanding digital health and telemedicine, and launching community-driven initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".