Good Governance Strategies for Human-Centered AI in Healthcare
Bibliographic record
Abstract
Many stakeholders are excited about the development and use of artificial intelligence (AI) in healthcare, particularly in contexts where most healthcare systems struggle with human and financial resource shortages. AI could provide professionals with the “gift of time” ( Topol, 2019a ) to give more compassionate care—and help patients access medical knowledge that permits them to take a more active role in health-related decisions. The average US nurse spends 25% of their work time on regulatory and administrative activities for which AI could partly assist ( Davenport & Kalakota, 2019 ). AI-enabled diagnoses and medical treatments could, in turn, provide substantive health improvements ( Topol, 2019a ). While many raise understandable concerns about how AI will impact care, market trends suggest health-related AI is here to stay: the global (non-health-specific) AI market is expected to reach a value of USD 1,811 billion by 2030 ( Research & Markets, 2023 ). Well-designed AI could improve the quality and efficiency of healthcare and services and population health ( Topol, 2019b ); however, one must seek ways to ensure it fulfills its beneficial aims, rather than creating a false sense of value.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".