Artificial intelligence for digital and computational pathology

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis; however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. Advances in digitizing human tissue slides and progress in artificial intelligence have boosted progress in the field of computational pathology. This Review consolidates recent methodological advances and provides future perspectives as the field expands to take on a broader range of clinical and research tasks. Supported by advances in artificial intelligence, curation of multi-institutional cohorts and the development of high-performance computing, computational pathology is now reaching clinical-grade performance for certain tasks. Artificial intelligence-based methods in computational pathology can be distinguished into methods for predicting clinical end points from tissue specimens and assistive tools for clinical or research tasks. Multiple instance learning is a rapidly growing paradigm for predicting clinical end points, such as disease diagnosis and molecular alterations, from whole-slide images. Computational pathology can be used for automating tasks that pathologists already perform in daily practice and for discovering morphological biomarkers for clinical outcomes of interest. Initiatives for collecting larger, well-curated and multimodal datasets, together with advances in artificial intelligence frameworks, are required for the clinical adoption of computational pathology tools. Supported by advances in artificial intelligence, curation of multi-institutional cohorts and the development of high-performance computing, computational pathology is now reaching clinical-grade performance for certain tasks. Artificial intelligence-based methods in computational pathology can be distinguished into methods for predicting clinical end points from tissue specimens and assistive tools for clinical or research tasks. Multiple instance learning is a rapidly growing paradigm for predicting clinical end points, such as disease diagnosis and molecular alterations, from whole-slide images. Computational pathology can be used for automating tasks that pathologists already perform in daily practice and for discovering morphological biomarkers for clinical outcomes of interest. Initiatives for collecting larger, well-curated and multimodal datasets, together with advances in artificial intelligence frameworks, are required for the clinical adoption of computational pathology tools.