Translational AI and Deep Learning in Diagnostic Pathology

Frontiers in Medicine

article

Ahmed Serag¹  Adrian Ion-Margineanu¹  Hammad Qureshi¹  Ryan McMillan¹  Marie-Judith Saint Martin¹  Jim Diamond¹  Paul O'Reilly¹  Peter Hamilton¹ 

[1] Life Sciences R&D Hub, United Kingdom

关键词: pathology;    digital pathology;    artificial intelligence;    computational pathology;    image analysis;    neural network;    deep learning;    machine learning;   

DOI  :  10.3389/fmed.2019.00185

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

【 授权许可】

CC BY