Artificial intelligence enabled applications in kidney disease

Sheetal Chaudhuri, Andrew Long, Hanjie Zhang, Caitlin Monaghan, John W. Larkin, Peter Kotanko, Shashi Kalaskar, Jeroen P. Kooman, Frank M. van der Sande, Franklin W. Maddux, Len A. Usvyat*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Web of Science)

Abstract

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

Original languageEnglish
Pages (from-to)5-16
Number of pages12
JournalSeminars in Dialysis
Volume34
Issue number1
Early online date13 Sep 2020
DOIs
Publication statusPublished - Jan 2021

Keywords

  • DIALYSIS PATIENTS
  • CLINICAL-OUTCOMES
  • ANEMIA MANAGEMENT
  • CO-MORBIDITY
  • CLASSIFICATION
  • MORTALITY
  • RISK
  • VALIDATION
  • PREDICTION
  • MEDICINE

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