From Asking “What” to “Says Who”: Examining Social, Political, and Economic Forces Shaping Artificial Intelligence in Healthcare
Bibliographic record
Abstract
Artificial intelligence (AI) is widely portrayed as a transformative innovation poised to revolutionise healthcare in the 21st century. Promoted by technology companies, this forward-looking narrative is often adopted uncritically by decision-makers, despite limited evidence of AI’s clinical effectiveness in real-world contexts and the persistence of numerous regulatory, professional, organisational, ethical, and governance challenges. In healthcare systems characterised by pluralism and complexity, the trajectory of AI is shaped by the strategic decisions and actions of diverse stakeholders who mobilise their social, political, and economic power and resources to influence dominant narratives about AI and to advance specific visions of the future of healthcare. This paper critically examines such influences by specifically focusing on how market imperatives, algorithmic logic, and the entrenched marginalisation of certain populations impact technology development and care delivery. It aims to investigate how particular actors and power dynamics affect AI research, commercialisation, and sustainable integration into healthcare systems. Because AI can reinforce and deepen existing power imbalances and inequalities, this paper calls for public policies grounded in social justice, fairness, and the public interest. These policies should aim to mitigate systemic harms and promote equity in healthcare.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".