Flight rules for clinical AI: lessons from aviation for human-AI collaboration in medicine

Правила полётов для клинического ИИ: уроки авиации для взаимодействия человека и ИИ в медицине
Ariel Yuhan Ong, David A. Merle, Andreas Pollreisz, Siegfried K. Wagner, Mertcan Sevgi, Pearse A. Keane, Roman Huemer, Julian Oehling, Markus Jäger, Josef Huemer
2026-01-31

aviation safety frameworkclinical artificial intelligencedigital copilothuman-AI collaborationscenario-based training
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.
1
Aviation’s automation experience shows that AI can improve safety and efficiency while introducing new vulnerabilities and misplaced trust in complex systems.
2
Medicine should shift from viewing clinical AI as an “autopilot” toward collaborating with a “digital copilot.”
3
Optimizing human–AI collaboration is presented as essential for translating AI integration into improved patient care.
4
Safe clinical AI integration requires aviation-inspired practices, including scenario-based training, clinician benchmarking, and minimum unaided practice.

human-AI collaboration in clinical care

safety, trust, training, benchmarking, and maintenance of clinician skills in AI-assisted medicine

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Publication Date
2026-01-31
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Authors
Ariel Yuhan Ong
David A. Merle
Andreas Pollreisz
Siegfried K. Wagner
Mertcan Sevgi
Pearse A. Keane
Roman Huemer
Julian Oehling
Markus Jäger
Josef Huemer
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