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Related Experiment Video

Updated: May 23, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

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

Negar Taheri1, Sahussapont J Sirintrapun1

  • 1Department of Pathology, Mass General Brigham, Harvard Medical School, Massachusetts, USA.

APMIS : Acta Pathologica, Microbiologica, Et Immunologica Scandinavica
|May 21, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise in placental pathology for improving diagnosis and predicting outcomes. AI can analyze placental morphology, addressing limitations in current methods and enhancing patient care.

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Area of Science:

  • Pathology
  • Medical Artificial Intelligence
  • Digital Pathology

Background:

  • Placental pathology diagnostics are limited by resource strain, interobserver variability, and a lack of scalable analytic tools.
  • Established diagnostic guidelines exist, but clinical demand for placental examination is increasing.
  • Current applications of artificial intelligence (AI) and digital pathology in this field are underdeveloped.

Purpose of the Study:

  • To review current and future applications of AI in placental pathology.
  • To examine challenges and potential solutions for AI implementation in this domain.
  • To highlight AI's potential to improve diagnostic reproducibility and predict adverse maternal-fetal outcomes.

Main Methods:

  • Synthesis of current literature on AI applications in placental pathology.

Related Experiment Videos

Last Updated: May 23, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

  • Analysis of AI's role in gross examination, villous maturation, lesion detection, and inflammation assessment.
  • Examination of challenges like data scarcity, reproducibility, interpretability, and potential solutions including human-in-the-loop workflows.

    • Main Results:

    • AI models demonstrate strong performance in automated placental gross image analysis, villous maturation assessment, and lesion detection.
    • AI effectively quantifies complex features in inflammatory lesion analysis and cell classification.
    • Placental morphology is machine-recognizable and quantifiable, indicating AI's potential.

    Conclusions:

    • AI-driven placental pathology can enhance diagnostic reproducibility and uncover novel disease mechanisms.
    • AI offers potential for earlier prediction of adverse maternal-fetal outcomes.
    • Clinical translation requires addressing dataset diversity, tissue variability, model interpretability, and strategic investment in digital infrastructure and collaboration.