Artificial intelligence-enabled decision support in nephrology

Tyler J. Loftus, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Yuanfang Ren, Benjamin S. Glicksberg, Jie Cao, Karandeep Singh, Lili Chan, Girish N. Nadkarni, Azra Bihorac

Research output: Contribution to journalReview articlepeer-review

22 Scopus citations


Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems — which use algorithms based on learned examples — may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.

Original languageEnglish
Pages (from-to)452-465
Number of pages14
JournalNature Reviews Nephrology
Issue number7
StatePublished - Jul 2022


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