Enhancing Healthcare with Digital Twins: A Comparative Approach Using AI and AI-Enhanced Digital Twins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research evaluates the effect of digital twins (DTs) on healthcare progress, especially in connection with Chronic Obstructive Pulmonary Disease (COPD). We contrast systems that use only AI and systems that use digital twins to evaluate improvements in the accuracy of prediction, real-time surveillance, and patient engagement. Our approach utilizes IoT sensors to record physiological data in real time with the aid of high-tech machine models. The results of our research suggest that the use of digital twins raises accuracy to 92.09% instead of 78.71, achieved exclusively through AI. This research explains how digital twins improve predictive analytics, and how it encourages more proactive medical treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| 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 it