"Hi, How Can I Help You?" Embracing Artificial Intelligence in Kidney Research

Am J Physiol Renal Physiol. 2023 Aug 17. doi: 10.1152/ajprenal.00177.2023. Online ahead of print.ABSTRACTIn recent years, biology and precision medicine have benefited from the major advancements in generating large-scale molecular and biomedical datasets and in analyzing those data using advanced machine learning algorithms. Machine learning applications in kidney physiology and pathophysiology include segmenting kidney structures from imaging data and predicting conditions like acute kidney injury or chronic kidney disease using electronic health records. Despite the potential of machine learning to revolutionize nephrology by providing innovative diagnostic and therapeutic tools, its adoption in kidney research has been slower than in other organ systems. Several factors contribute to this underutilization. The complexity of the kidney as an organ, with intricate physiology and specialized cell populations, makes it challenging to extrapolate bulk omics data to specific processes. Additionally, kidney diseases often present with overlapping manifestations and morphological changes, making diagnosis and treatment complex. Moreover, kidney diseases receive less funding compared to other pathologies, leading to lower awareness and limited public-private partnerships. To promote the use of machine learning in kidney research, this review provides an introduction to machine learning and reviews its notable applications in renal research, such as morphological analysis, omics da...
Source: Am J Physiol Renal P... - Category: Urology & Nephrology Authors: Source Type: research