Regulation of Artificial Intelligence in Healthcare – A Global View
Bibliographic record
Abstract
As artificial intelligence (AI) becomes a cornerstone of healthcare and medicine, the global focus has shifted from innovation to regulation. Across the world, efforts to regulate AI are rapidly evolving as governments and legal systems struggle to keep pace with the advances and novel applications of AI in healthcare. To support regulators and stakeholders in this task, we have examined and evaluated global AI regulatory frameworks focusing on the efforts of international organizations (WHO, EU) and individual nations (USA, UK, Australia, and Canada) to analyze the progress made in this area. While stakeholders are advancing legislation to guide AI development and deployment, gaps persist in implementation, oversight, and long-term monitoring, especially within the healthcare sector. Despite competing economic and political realities, the dilemma between centralized and decentralized policies continues to define international efforts. However, ethical standards must guide regulation, ensuring flexible yet principled frameworks that strike a balance between autonomy and human oversight. As patient data increasingly fuels AI systems, ensuring data security and patient privacy is paramount. Regulatory fragmentation, medico-legal uncertainty, and a lack of uniform best practices challenge the safe and equitable use of AI technologies. Key concerns include preserving patient autonomy, ensuring transparency, managing bias, securing data, and maintaining human oversight in medical decision-making. We suggest that future regulatory efforts be built on collaboration between stakeholders around the globe and concentrate on providing good governance, enhancing patient safety and ensuring the responsible use of AI in healthcare and medicine.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".