Artificial Intelligence (AI) in Nephrology

  • Muzaffar Wani SKIMS
  • Jawad Iqbal Rather Department of Nephrology, SKIMS
  • Imtiaz Ahmad Wani Department of Nephrology, SKIMS
  • Mohd Ashraf Bhat Department of Nephrology, SKIMS
  • Manzoor Parray Department of Nephrology, SKIMS
  • Rayees Yousuf Department of Nephrology, SKIMS
Keywords: Artificial intelligence, Kidney Disease, Treatment plan

Abstract

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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 the form of search engines like Google or home assistants Alexa and, nowadays, OpenAI with its chatbot. AI utility in the field of Nephrology is immense, particularly in the areas of diagnosis, treatment, and prediction of prognosis in various kidney diseases. Instead of the human brain 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. It is mandatory to train nephrologists in the fundamentals of AI because time has come to shift from the traditional practice of decision-making in kidney diseases to AI-based tools to quickly analyse patients’ information and come to a quick decision.

 

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Author Biographies

Jawad Iqbal Rather, Department of Nephrology, SKIMS

DM scholar

Imtiaz Ahmad Wani, Department of Nephrology, SKIMS

Professor

Mohd Ashraf Bhat, Department of Nephrology, SKIMS

Professor

Manzoor Parray, Department of Nephrology, SKIMS

Assistant Professor

Rayees Yousuf, Department of Nephrology, SKIMS

Assistant Professor

Published
2023-09-30
How to Cite
1.
Wani M, Rather JI, Wani IA, Bhat MA, Parray M, Yousuf R. Artificial Intelligence (AI) in Nephrology. jms [Internet]. 2023Sep.30 [cited 2026Oct.2];26(3):3-. Available from: https://www.jmsskims.org/index.php/jms/article/view/1297
Section
Review Articles

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