A hospital’s AI flagged problems correctly 94% of the time, but that success hid a dangerous outcome: radiologists stopped independently evaluating scans and became rubber stamps, missing the machine’s rare but critical errors. This episode explores the automation bias paradox and why better models can make human oversight worse if workflows and governance aren’t intentionally designed.

Experts discuss five collaboration models, practical design fixes (independent judgment first, visible uncertainty, proportionate friction, AI‑free practice), and governance measures to preserve capacity, authority, and responsibility at scale. Actions for leaders to take this week are included.

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