AI-assisted placenta pathology in clinical use: Barriers and opportunities

Placental pathology provides critical diagnostic information following pregnancy complications and has the potential to inform mother and infant clinical care. Yet the field faces substantial challenges, including workforce shortages, high inter-observer variability, inconsistent reporting, and ongoing debate regarding clinical utility. Artificial intelligence (AI) and computer vision methods offer potential solutions through automated analysis of both gross photography and digital histology. Recent studies demonstrate technical feasibility across tasks including gestational age prediction, cellular and structural quantification, and lesion detection. Near-term clinical applications should prioritise standardising measurements, reducing inter-observer variability, and accelerating report generation through assistive workflows to deliver timely results for clinical decision-making. Over the longer term, AI-derived placental phenotypes could enable prognostic research linking placental features to maternal and childhood outcomes, supporting a shift from diagnostic to prognostic clinical pathways. However, significant barriers impede clinical translation. These include the infrastructure and deployment costs associated with digital pathology and AI, regulatory requirements, and challenges specific to placenta pathology such as limited open-source datasets, indication bias in clinical samples, difficulty integrating information across placental compartments, inconsistent data linkage, and uncertainty regarding which features should be measured. Together, these factors contribute to high development costs and limit the availability of clinically deployable models. This review examines both the opportunities for AI and computer vision in placental pathology and the barriers to their translation, and proposes priorities for the pathology community. Addressing these priorities would position AI to transform placental pathology from a resource-limited diagnostic service into a scalable, data-driven discipline that improves immediate clinical care and enables discovery-driven advances in maternal and child health.