Data Ownership in the AI-Powered Integrative Health Care Landscape
Bibliographic record
Abstract
In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".