Journal
GENES CHROMOSOMES & CANCER
Volume 62, Issue 11, Pages 685-697Publisher
WILEY
DOI: 10.1002/gcc.23192
Keywords
artificial intelligence; digital pathology; digital transformation; machine learning; patient care; personalized medicine
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Pathology laboratories are utilizing digital pathology systems to enhance patient care. These systems allow pathologists to perform tasks digitally rather than using traditional glass slides and microscopes. The integration of image analysis and machine learning enables computer assisted diagnostics and has the potential to greatly improve pathology practices and patient care.
Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.
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