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 tumors 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 favor clinician intuition when it is honed by experience.

Key points of the research

• Hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment often fail to adequately represent the complex, nonlinear, and heterogeneous nature of kidney pathophysiology.
• Artificial intelligence (AI)-enabled decision support systems use algorithms that learn from examples to accurately represent complex pathophysiology, including kidney pathophysiology, offering opportunities to enhance patient-centred diagnostic, prognostic and treatment approaches.
• Contemporary AI applications can accurately predict kidney injury before the development of measurable biochemical changes, identify modifiable risk factors, and match or exceed human accuracy in recognizing kidney pathology on imaging studies.
• Advances in the past few years suggest that AI models have potential to make real-time, continuous recommendations for discrete actions that yield the greatest probability of achieving optimal kidney health outcomes.
• Optimizing the clinical integration of AI-enabled decision-support in nephrology will require multidisciplinary commitment to ensure algorithm fairness and the building of an AI-competent medical workforce.
• AI-enabled decision support should preserve the pre-eminence of human wisdom and intuition in clinical decision-making by augmenting rather than replacing interactions between patients, caregivers, clinicians and data.

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