Artificial intelligence and computational pathology.
Miao CuiDavid Y ZhangPublished in: Laboratory investigation; a journal of technical methods and pathology (2021)
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. 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. 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. 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. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.
Keyphrases
- big data
- artificial intelligence
- electronic health record
- end stage renal disease
- newly diagnosed
- ejection fraction
- machine learning
- chronic kidney disease
- clinical practice
- prognostic factors
- mass spectrometry
- magnetic resonance imaging
- social media
- risk assessment
- health information
- quality improvement
- medical students
- global health