Modernizing Nephrology through Artificial Intelligence

Rather, Jawad Iqbal and Wani, Muzafar Maqsood and Rasheed, Rabiya (2024) Modernizing Nephrology through Artificial Intelligence. In: Advancement and New Understanding in Medical Science Vol. 6. B P International, pp. 106-119. ISBN 978-81-970279-0-1

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Abstract

Artificial intelligence (AI) is an established branch of computer sciences that utilizes computer algorithms to solve problems that otherwise is done by humans. It is there in our daily routines be it in form of search engines like Google or to home assistants Alexa and, nowadays, OpenAI with its chatbot. AI has the potential to revolutionize the diagnosis, prediction, and treatment of glomerular disease by leveraging its ability to analyze large datasets and identify patterns. AI utility in the field of Nephrology is immense, particularly in the areas of diagnosis, treatment, and prediction of various kidney diseases as well as its ability to improve diagnostic accuracy. The reason of early and precise identification of acute kidney injury (AKI) is of paramount importance for the ascertaining the overall morbidity and mortality of a patient. Instead of the human brain the machine learning algorithms can help to identify early signs of kidney disease by recognizing patterns in patient demographic data, lab results, imaging, and medical history, and hence allow timely diagnosis and prompt initiation of treatment plans that ultimately improve patient outcome. AI holds the promise of advancing personalized medicine to new levels. AI will augment in decision making and should best be labelled as “Augmented Intelligence” once it comes to its role in medicine. It is mandatory to train nephrologists in the fundamentals of AI because time has come to shift from the traditional practice of decision-making of kidney diseases to AI based tools to quickly analyse patients’ information and come to a quick decision.

Item Type: Book Section
Subjects: Eprint Open STM Press > Medical Science
Depositing User: Unnamed user with email admin@eprint.openstmpress.com
Date Deposited: 12 Feb 2024 09:44
Last Modified: 12 Feb 2024 09:44
URI: http://library.go4manusub.com/id/eprint/2032

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