Hutchinson J Ciaran, Picarsic Jennifer, McGenity Clare, Treanor Darren, Williams Bethany, Sebire Neil J
Great Ormond Street Hospital, London, UK.
Children's Hospital of Pittsburgh of University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Pediatr Dev Pathol. 2025 Mar-Apr;28(2):91-98. doi: 10.1177/10935266241299073. Epub 2024 Nov 18.
The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and deep learning, impacts researchers, clinicians, and industry due to promising performance and clinical potential. Digital pathology is now a proven technology, enabling generation of high-resolution digital images from glass slides (whole slide images; WSI). WSIs facilitates AI-based image analysis to aid pathologists in diagnostic tasks, improve workflow efficiency, and address workforce shortages. Example applications include tumor segmentation, disease classification, detection, quantitation and grading, rare object identification, and outcome prediction. While advancements have occurred, integration of WSI-AI into clinical laboratories faces challenges, including concerns regarding evidence quality, regulatory adaptations, clinical evaluation, and safety considerations. In pediatric and developmental histopathology, adoption of AI could improve diagnostic efficiency, automate routine tasks, and address specific diagnostic challenges unique to the specialty, such as standardizing placental pathology and developmental autopsy findings, as well as mitigating staffing shortages in the subspeciality. Additionally, AI-based tools have potential to mitigate medicolegal implications by enhancing reproducibility and objectivity in diagnostic evaluations. An overview of recent developments and challenges in applying AI to pediatric and developmental pathology, focusing on machine learning methods applied to WSIs of pediatric pathology specimens is presented.
人工智能（AI）融入医疗保健领域正日益成为主流。借助人工智能和深度学习等数字技术，由于其有前景的性能和临床潜力，对研究人员、临床医生和行业都产生了影响。数字病理学现已成为一项成熟技术，能够从玻璃切片（全切片图像；WSI）生成高分辨率数字图像。全切片图像有助于基于人工智能的图像分析，以协助病理学家完成诊断任务、提高工作流程效率并解决劳动力短缺问题。示例应用包括肿瘤分割、疾病分类、检测、定量和分级、罕见物体识别以及结果预测。尽管已经取得了进展，但将全切片图像 - 人工智能集成到临床实验室仍面临挑战，包括对证据质量、监管适应、临床评估和安全考虑等方面的担忧。在儿科和发育组织病理学中，采用人工智能可以提高诊断效率、使常规任务自动化，并应对该专业特有的特定诊断挑战，例如标准化胎盘病理学和发育解剖结果，以及缓解该亚专业的人员短缺问题。此外，基于人工智能的工具具有通过提高诊断评估的可重复性和客观性来减轻法医学影响的潜力。本文概述了将人工智能应用于儿科和发育病理学的最新进展和挑战，重点关注应用于儿科病理标本全切片图像的机器学习方法。
