ReviewNPJ digital medicine2026
Flight rules for clinical AI: lessons from aviation for human-AI collaboration in medicine.
Review in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- EACTS Expert Consensus Document on Civil Aviation Following Cardiothoracic Surgery and Transcatheter Procedures.Interdisciplinary cardiovascular and thoracic surgery · 2026Review
- Reconciling how clinical reasoning is learned in the age of artificial intelligence.NPJ digital medicine · 2026Article
- AI-induced never-skilling in medical education.Nature medicine · 2026Review
- Why Medical Education Without Artificial Intelligence Still Matters: A Neuroscience-Informed Perspective.JMIR medical education · 2026Article
- The Long road to AI-physician partnership starts with learners: a map for AI in medical education.BMJ digital health & AI · 2026Review
- Descriptive analysis of prescription interception patterns: characterizing medication safety risks in an outpatient setting.Frontiers in pharmacology · 2026Article
- Rethinking AI in clinical decision support: a framework for reciprocal human-AI interaction.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The parallels between medicine and aviation are well-recognised. The aviation industry's early experience with automation improved safety and efficiency, but simultaneously introduced new vulnerabilities and occasionally created misplaced trust in complex systems. Aviation has developed a robust safety framework in response to these costly lessons. In this Perspective, which draws from the experiences of clinicians and aviation experts, we argue that it is now time for the medical community to consider how we can learn from these lessons as artificial intelligence (AI) becomes increasingly integrated into clinical care. We propose that this requires a shift in perspective from AI as "autopilot" to collaboration with a "digital copilot", as well as considerations of practicalities such as scenario-based training, clinician benchmarking, and minimum unaided practice, with the ultimate aim of optimising human-AI collaboration to improve patient care.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.