THE SCRUTINY OF AI, ML, BIG DATA,DEEP LEARNING AND OTHER TECHNICAL VOWS AND CALLS IN NEPHROLOGY

Abstract

Telepathology, which was first used in the 1960s, established the possibility of data exchange and communication for diagnostic evaluation. Human kidney biopsies done on individuals with primary renal failure are the major source of benign renal histopathology data. Most AI applications are still in the conceptual design phase. From computer vision to genomic data gathering, deep learning models are progressively have been used to analyze biological data. Initiatives such as CNN, FCNN, GAN deep hierarchical learning, and recurrent neural networks seem to be well. Over the next five years, the biomedicine material is expected to quadruple every 18–24 months. Going to follow the coronavirus pandemic, digitalization of treatments to intensify, especially in those societies that are always heavily reliant on technology, With Rising numbers of biopsies, data held in archives throughout the world are hardly digitized, and metadata is not standardized. To make matters harder, the development of robust algorithms that are sensitive to inter-laboratory differences necessitates the use of multicenter samples. In biology and wellness, ethical norms and data safeguards that do not endanger patient value are badly required. The kidney domain lags below most fields in terms of big data use in studies. This void, on the other hand, presents a chance for experts to pursue a career in nephrology and have a substantial impact on the discipline.