Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System

被引：12

作者：

Gu, Hongyan ^{[1
]}

Yang, Chunxu ^{[1
]}

Haeri, Mohammad ^{[2
]}

Wang, Jing ^{[3
]}

Tang, Shirley ^{[1
]}

Yan, Wenzhong ^{[1
]}

He, Shujin ^{[3
]}

Williams, Christopher Kazu ^{[4
]}

Magaki, Shino ^{[4
]}

Chen, Xiang 'Anthony' ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Los Angeles, CA 90095 USA

[2] Univ Kansas, Med Ctr, Kansas City, KS USA

[3] Capital Med Univ, Beijing Tongren Hosp, Beijing, Peoples R China

[4] UCLA, David Geffen Sch Med, Los Angeles, CA USA

来源：

PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023 | 2023年

基金：

美国国家科学基金会;

关键词：

Human-AIcollaboration; digitalpathology; navigation; medicalAI; VISUAL-SEARCH; IMAGE; SLIDES;

D O I：

10.1145/3544548.3580694

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists' navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists' domain knowledge, we designed NAVIPATH - a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that navigation was more consistent with NaviPath, which can improve the overall examination quality.

引用

页数：19

共 85 条

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