Artificial intelligence in digital pathology - time for a reality check

被引:0
|
作者
Aggarwal, Arpit [1 ,2 ]
Bharadwaj, Satvika [1 ,2 ]
Corredor, German [1 ,2 ,3 ]
Pathak, Tilak [4 ]
Badve, Sunil [5 ]
Madabhushi, Anant [1 ,2 ,3 ]
机构
[1] Emory Univ, Wallace H Coulter Dept Biomed Engn, Atlanta, GA 30322 USA
[2] Georgia Inst Technol, Atlanta, GA 30332 USA
[3] Atlanta Vet Affairs Med Ctr, Atlanta, GA 30033 USA
[4] Emory Univ, Dept Biomed Engn, Atlanta, GA USA
[5] Emory Univ Sch Med, Dept Pathol & Lab Med, Atlanta, GA USA
基金
美国国家卫生研究院;
关键词
PROSTATE-CANCER; RISK STRATIFICATION; FOUNDATION MODEL; SURVIVAL; FEATURES;
D O I
10.1038/s41571-025-00991-6
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
The past decade has seen the introduction of artificial intelligence (AI)-based approaches aimed at optimizing several workflows across many medical specialties. In clinical oncology, the most promising applications include those involving image analysis, such as digital pathology. In this Perspective, we provide a comprehensive examination of the developments in AI in digital pathology between 2019 and 2024. We evaluate the current landscape from the lens of technological innovations, regulatory trends, deployment and implementation, reimbursement and commercial implications. We assess the technological advances that have driven improvements in AI, enabling more robust and scalable solutions for digital pathology. We also examine regulatory developments, in particular those affecting in-house devices and laboratory-developed tests, which are shaping the landscape of AI-based tools in digital pathology. Finally, we discuss the role of reimbursement frameworks and commercial investment in the clinical adoption of AI-based technologies. In this Perspective, we highlight both the progress and challenges in AI-driven digital pathology over the past 5 years, outlining the path forward for its adoption into routine practice in clinical oncology.
引用
收藏
页码:283 / 291
页数:9
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