Application of Artificial Intelligence in Pathology: Trends and Challenges

被引：52

作者：

Kim, Inho ^{[1
]}

Kang, Kyungmin ^{[1
]}

Song, Youngjae ^{[1
]}

Kim, Tae-Jung ^{[2
]}

机构：

[1] Catholic Univ Korea, Coll Med, 222 Banpo Daero, Seoul 06591, South Korea

[2] Catholic Univ Korea, Dept Hosp Pathol, Yeouido St Marys Hosp, Coll Med, 10,63 Ro, Seoul 07345, South Korea

来源：

DIAGNOSTICS | 2022年 / 12卷 / 11期

基金：

新加坡国家研究基金会;

关键词：

artificial intelligence; computational pathology; digital pathology; histopathology image analysis; deep learning; TUMOR-INFILTRATING LYMPHOCYTES; DIGITAL PATHOLOGY; IMAGE-ANALYSIS; STROMA RATIO; PROGNOSTIC VALUE; BREAST; CANCER; SYSTEM; CLASSIFICATION; VALIDATION;

D O I：

10.3390/diagnostics12112794

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows.

引用

页数：20

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