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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.
Bondi et al. (Sun,) studied this question.