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Abstract
Personalized medicine requires the integration and analysis of vast amounts of patient data in order to provide a personalized approach. Digital pancreatology and Pancreomics facilitate personalized recommendations in the diagnosis and treatment of pancreatic diseases by analyzing data using machine learning methods to exploit the potential of this data in a similar way to Pathomics, Radiomics, and Genomics.
The aim of this article is to present a brief overview of the modern capabilities of digital medicine for the diagnosis and treatment of various pancreatic diseases.
The studies included in the review demonstrate the promising results of machine learning technologies that can both facilitate clinical prediction and decision-making and be used to interpret clinical laboratory, radiological, and endoscopic images in pancreatology and medicine in general.
Digital pancreatology and Pancreomics may be promising concepts for the analysis and prediction of the course of pancreatic diseases in the contemporary digital era.
References
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