Harmonizing Artificial Intelligence Governance; A Model for Regulating a High-risk Categories and Applications in Clinical Pathology: The Evidence and some Concerns
Bibliographic record
Abstract
The Canadian healthcare system, grappling with issues like systemic and intelligently established structural anti-black racism, including indigenous nations; even within Pathology and Laboratory Medicine Communities: and deteriorating outcomes, sees potential in AI to address challenges, though concerns exist regarding exacerbating discriminatory practices. In clinical pathology, AI demonstrates superior diagnostic accuracy compared to pathologists in a study, emphasizing its potential to improve healthcare. However, AI governance is crucial to navigating ethical, legal, and societal concerns. The Royal College of Physicians of Canada acknowledges the transformative impact of AI in healthcare but stresses the need for responsible AI tools co-developed by diverse teams. Despite positive attitudes towards AI in healthcare, concerns about patient safety, privacy, and autonomy highlight the necessity for comprehensive education, engagement, and collaboration. Legal concerns, including liability and regulation, pose challenges, emphasizing the need for a robust regulatory framework. AI application in healthcare is categorized as high-risk, demanding stringent regulation to ensure safety, efficacy, and fairness. A parallel is drawn to drug regulation processes, suggesting a similar approach for AI. The lack of transparency in AI-based decision-making raises ethical questions, necessitating measures to address biases and ensure patient privacy. Social accountability is crucial to prevent AI from exacerbating health disparities and harming marginalized communities. In conclusion, while AI offers potential benefits in clinical pathology, a cautious approach with comprehensive governance measures is essential to mitigate risks and ensure ethical AI integration into 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 | 0.188 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.067 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".