Advancing Placental Pathology With Artificial Intelligence: Toward Reproducible and Predictive Diagnostics

Placental pathology remains underdeveloped in its application of artificial intelligence (AI) and digital pathology, despite established diagnostic guidelines and increasing clinical demand. Determining which placentas require histologic examination continues to strain staffing and resources, while histologic interpretation is limited by high interobserver variability and a lack of scalable analytic tools. This review synthesizes current applications and future opportunities for AI in placental pathology across multiple domains, including gross examination, villous maturation, lesion detection, and inflammation. We examine challenges related to data scarcity, reproducibility, and interpretability, and discuss potential solutions such as human-in-the-loop workflows. Recent studies demonstrate strong performance of AI models in automated placental gross image analysis, villous maturation assessment, and lesion detection, with inflammatory lesion analysis and cell classification further highlighting AI's ability to quantify complex, traditionally subjective features. Collectively, these findings show that placental morphology is machine-recognizable and quantifiable. AI-driven placental pathology has the potential to improve diagnostic reproducibility, uncover novel disease mechanisms, and enable earlier prediction of adverse maternal-fetal outcomes. Clinical translation will require addressing dataset diversity, tissue-processing variability, and model interpretability, as well as strategic investment in digital infrastructure, standardization, and interdisciplinary collaboration.