Toward responsible artificial intelligence in health: regulatory structures and power dynamics of the big tech industry in the United States
Bibliographic record
Abstract
Artificial intelligence (AI) offers potential strategies to address existing challenges facing health systems in the United States (U.S.). However, the development of the AI market for health care over the past decade also poses risks to public value in the short- and long-term. In this commentary, we describe the nature of large technology companies’ interface with health care in the U.S., outlining their roles in the context of their leadership in the platform economy. First, we describe the risks associated with the potential dominance of Big Tech companies in healthcare and outline the short-term context for regulating AI as it relates to Big Tech’s role in healthcare. We then explore the possibilities of regulatory approaches that might encourage the anticipation of risks and enforcement of responsible technology practices while retaining the goal of enhancing public value as a primary aim of healthcare policy.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".