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.

肾脏病理生理学通常是复杂的、非线性的和异质的，这限制了假设演绎推理和线性统计方法在诊断和治疗中的实用性。新的证据表明，人工智能 (AI) 支持的决策支持系统（使用基于学习示例的算法）可能在肾脏病学中发挥重要作用。当代人工智能应用可以在显着的生化变化发生之前准确预测急性肾损伤的发生；可以识别慢性肾脏病发病和进展的可改变的危险因素；在影像学研究中识别肾肿瘤的准确性可以达到或超过人类；并可能增强肾移植后的预测和决策。未来的人工智能应用程序有可能为离散的行动提供实时、连续的建议，并最大可能地实现最佳的肾脏健康结果。实现人工智能应用的临床整合需要多学科合作，以确保算法公平性，克服临床实施的障碍，并建立一支具备人工智能能力的劳动力队伍。人工智能支持的决策支持应该保持智慧的优势并增强而不是取代人类决策。通过将直觉与客观预测和分类结合起来，这种方法应该有利于临床医生通过经验磨练的直觉。