Design of Interactive Artificial Intelligence for Early Cognitive Diagnosis
Bibliographic record
Abstract
Due to the aging population in South Korea, the proportion of elderly people aged 65 and over is expected to increase from 14.9% in 2019 to 46.5% by 2067. The number of elderly people per 100 working-age population (15-64 years old) is also anticipated to rise to 102.4 by 2067. Population aging is recognized as a social issue, leading to problems such as increased chronic diseases, higher levels of elderly isolation, and insufficient medical infrastructure. To solve the problem of cognitive decline, such as dementia due to the aging of the population, research is actively being conducted in various fields, such as simulating cognitive ability and learning, inference, prediction, and problem-solving using artificial intelligence deep learning technology in the form of a fusion of artificial intelligence and realistic content technology. This study is on an interactive cognitive early diagnosis training system using artificial intelligence. The speech recognition technology for early cognitive diagnosis uses Selvas AI (Artificial Intelligence)'s speech recognition STT (Speech to Text)-TTS (Text to Speech). AI speech recognition interaction can increase psychological safety through conversation with users. It evaluates cognition (dementia) using MMSE (Mini-Mental State Examination)-K (DS), and it is a system that evaluates seven cognitive areas through the CERAD (Consortium to Establish a Registry for Alzheimer Disease)-K analysis system. The design of interactive artificial intelligence for early cognitive diagnosis aims to improve the cognitive function and daily living abilities of the elderly population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".