Artificial intelligence in pharmaceutical drug product quality control and assurance

Machine learning has moved from proof of concept to routine operation in pharmaceutical quality control, yet the autonomy it is granted narrows sharply as products approach release. In this narrative review, we propose that this unevenness reflects not model capability but the regulatory burden an output must clear before it can be acted on, a pattern we term the validation‐barrier gradient. We score this barrier across four stages on criteria covering decision criticality under good manufacturing practice, validation and data‐integrity load, locus of legal liability, and patient‐safety consequence. The composite barrier rises as observed autonomy falls. At final product inspection, deep vision systems already detect defects at line speed, often on small single‐site datasets, but the authority to release a batch still rests with a qualified person. This gap between what a model can do and what it is allowed to decide cannot be explained by task difficulty alone. Closing it requires not further accuracy gains but shared benchmarks, routine robustness reporting, and change‐control pathways allowing validated models to be updated rather than frozen.