From Detection to Prediction: The NDE 4.0 Transition

This review traces the four-generation evolution of non-destructive evaluation (NDE 1.0–4.0) and audits where the field genuinely stands today. The central finding is that statistically qualified probability of detection (POD), as defined in MIL-HDBK-1823A and related frameworks, is not interchangeable with machine-learning metrics such as accuracy or F1-score; the two answer different questions and rest on different statistical foundations. Reported AI performance on curated datasets does not, by itself, predict field reliability because domain shift, sensor variability, and class imbalance change the inspection signal once a model leaves the lab. Six recurring barriers limit industrial uptake: scarce open benchmark datasets, domain shift, weak interoperability, explainability constraints, cybersecurity exposure, and the lack of broadly accepted code provisions for AI-derived accept/reject decisions. The oil and gas sector is used as a case study because it combines high inspection volume, severe operating environments, mature risk-based inspection practice, and strong regulatory conservatism. NDE 4.0 is technically credible; its wider acceptance in safety-critical industries will be earned through representative field validation, auditable model governance, standardised data structures, and qualification pathways—not through stronger laboratory accuracy claims.