Taking Off with AI: Lessons from Aviation for Healthcare
Bibliographic record
Abstract
Artificial intelligence (AI) stands to improve healthcare through innovative new systems ranging from diagnosis aids to patient tools. However, such “Health AI” systems are complicated and challenging to integrate into standing clinical practice. With advancing AI, regulations, practice, and policies must adapt to a wide range of new risks while experts learn to interact with complex automated systems. Even in the early stages of Health AI, risks and gaps are being identified, like severe underperformance of models for minority groups and catastrophic model failures when input data shift over time. In the face of such gaps, we find inspiration in aviation, a field that went from highly dangerous to largely safe. We draw three main lessons from aviation safety that can apply to Health AI: 1) Build regulatory feedback loops to learn from mistakes and improve practices, 2) Establish a culture of safety and openness where stakeholders have incentives to report failures and communicate across the healthcare system, and 3) Extensively train, retrain, and accredit experts for interacting with Health AI, especially to help address automation bias and foster trust. Finally, we discuss remaining limitations in Health AI with less guidance from aviation.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".