Local Conformal Auditing Exposes and Restores Reliability of AI-ECG Atrial Fibrillation Detection Under External Deployment Shift

BACKGROUND: AI-enabled electrocardiography (AI-ECG) can detect atrial fibrillation (AF), but safe deployment depends on calibration, subgroup reliability, and explicit error guarantees, not on discrimination alone. OBJECTIVE: To test whether the conformal-prediction coverage guarantee of an AF ECG classifier survives external deployment shift, and whether a small local labeled audit restores it without retraining. METHODS: A four-class 12-lead rhythm classifier trained on PTB-XL (Germany; 7,831 ECGs, 7,164 patients) was evaluated unchanged on Chinese (Chapman-Shaoxing; n=2,289) and US (Georgia; n=1,990) cohorts. We measured discrimination, expected calibration error (ECE), 90%-target split-conformal coverage, subgroup coverage, label-free shift corrections, local recalibration across audit sizes, and an AF auto-clear policy with finite-sample risk control. RESULTS: AF AUC remained 0.998 (95% CI 0.997-0.999) in China but fell to 0.936 (0.919-0.952) in the US cohort. ECE rose from 0.051 to 0.197 and 0.237, and source-calibrated coverage fell from 0.901 to 0.758 and 0.722 (both P<0.001). Coverage was lower in Chinese men than women (0.707 vs 0.822; difference 0.115, 95% CI 0.081-0.148). Label-free corrections failed. Local recalibration restored mean coverage to 0.90 with 50 labeled ECGs but with wide variability (95% simulation interval 0.82-0.97), narrowing to 0.86-0.94 at 200 ECGs. Local AF risk control certified auto-clearing of 68% of held-out ECGs in China but abstained in the US cohort. CONCLUSION: AI-ECG uncertainty can fail silently despite high AUC. A local conformal audit detects when uncertainty estimates are untrustworthy and recalibrates the uncertainty layer without changing the classifier, converting trust into a measurable, subgroup-aware, task-specific guarantee.