Review of assessment methods for dementia
Bibliographic record
Abstract
Abstract Background More than 50 million people worldwide live with various types of dementia, which incorporates a progressive decline in patients' cognition. Early detection of dementia in older adults is crucial and helps them apply for intervention programs. Numerous assessment methods have been developed to be used in clinical practice and help clinicians accurately detect dementia and diagnose its types. The recent advancement in the artificial intelligence domain has revolutionized the early detection of dementia and allowed clinicians to use AI‐based assessment technologies for detecting dementia at its very mild decline stage. The AI community aims to develop valid, efficient, practical, reliable, and accurate AI‐based assessment technologies to detect dementia; thus, to help the community, this paper reviews clinical assessment methods and recently developed AI‐based assessment technologies, particularly AI‐based language and speech assessment techniques. Moreover, it collects information about available datasets, python libraries for developing AI assessment methodologies to detect dementia. Method We have extracted around 300 peer‐reviewed articles from PubMed and MEDLINE related to AI‐based assessment technologies (i.e., focusing on automated language and speech‐based assessment methods) and cognitive, behavioral, neurophysiological, functional assessment methods. Result By reviewing more than 50 various clinical and AI assessment methods, we listed the advantages and limitations of clinical and AI assessment methods and available machine learning datasets, python scripts, and libraries to develop AI assessment methods to detect dementia. Conclusion The paper could provide valuable information for the AI community to have better progress in developing quick, accurate, multicultural, practical assessment technologies with high specificity and sensitivity. The paper can be beneficial for mental health clinicians to learn about known AI‐based assessment methods to detect dementia.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".