Artificial intelligence and machine learning in nephropathology

Abstract Artificial intelligence (AI) in nephrology is an umbrella term for technologies emulating a nephropathologist’s ability to extract information about diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be employed to analyse of a wide variety of biopsy-related data, this review will focus on whole slide images (WSIs) traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including ( 1 ) widespread introduction of glass slide scanners, ( 2 ) data servers in pathology departments worldwide, and ( 3 ) through greatly improved computer hardware to enable AI training. In this paper, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Since AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly on the example of oncopathology. Moreover, we discuss the technological obstacles, as well as the current stakeholder and regulatory concerns for developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists, rather than making them redundant.