AI-powered speech device as a tool for neuropsychological assessment of an older adult population: A preliminary study
Bibliographic record
Abstract
As the older adult population continues to expand, the demands on the healthcare system intensifies, necessitating the development of technologies that effectively accommodate the requirements of older adults. While Artificial Intelligence (AI) systems hold promise as a solution, they have not been designed to accommodate the sensory and cognitive changes typical of aging individuals. The current study investigates the use of an AI-powered communication device for the assessment of neuropsychological tests to an older adult population. Twenty-four (twelve females) older adult participants completed three memory tasks using the AI device: logical memory, poem recall, and the backward and sequencing digit span tests. Significant negative correlations were found between the age of the participants and performance on the Logical memory and digit span tests. The AI device effectively identified age-related memory changes comparable to those observed with human administrators. Implementing this technology in healthcare offers several advantages: alleviating healthcare professionals' workload, improving standard of care by reaching underserved populations, and facilitating continuous screening for early identification of prodromal stages of neurodegenerative diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".