The Camelyon Grand Challenge 2016 (CAMELYON16 challenge), is a worldwide machine learning-based program to evaluate new algorithms for the automated detection of cancer in hematoxylin and eosin (H&E)-stained whole-slide imaging (WSI), has achieved encouraging results with a 92.4% sensitivity in tumor detection rate. The most important advantage of the computational pathology is to reduce errors in diagnosis and classification.
AI technologies have the ability to handle the gigantic quantity of data created throughout the patient care lifecycle to improve pathologic diagnosis, classification, prediction, and prognostication of diseases. The primary forces and limitations in this field are: (1) a shortage of experienced pathologists and the limitation of global health care resources (2) the ever increasing amount of health data available, including digital images, omics, clinical records, and patient demographic information, being generated through the process of patient care (3) the increased complexity that is created in managing and integrating the data across different sources in order to maximize patient care and (4) machine learning-based algorithms need to be efficiently harnessed in order to process and understand the big data. AI-based computational pathology as an emerging discipline has recently shown great promise to increase both the accuracy and availability of high-quality health care to patients in many medical fields. AI should be able to perform tasks that normally require human intelligence, such as visual perception, decision-making, and communication. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.Īrtificial intelligence (AI) refers to the simulation of the human mind in computer systems that are programmed to think like humans and mimic their actions such as learning and problem-solving. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care.
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception.
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