Artificial intelligence-enabled decision support in nephrology

Abstract

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.

Fig. 1:. Artificial intelligence systems through the lens of a learning healthcare system.

From: Artificial intelligence-enabled decision support in nephrology The learning cycle framework describes how communities learn. Communities use data to generate locally relevant knowledge and use that knowledge to inform changes in practice. This process generates data that is used to assess changes in the quality of care.

Fig. 2:. Lifecycle of artificial intelligence for decision support.

From: Artificial intelligence-enabled decision support in nephrology The lifecycle of artificial intelligence (AI) for clinical decision support is an integrated, iterative and complex series of processes. Building a model for clinical decision support often relies on the use of retrospective data. First, the cohort of interest must be investigated using inclusion and exclusion criteria. Second, relevant data elements must be extracted and their quality and conformation to modelling requirements ensured. Training a model to predict or classify an outcome between cases and controls requires several steps. Internal validation refers to the process of refining a model on a training subset of the cohort through such processes such as cross-validation. During the model refinement stage, model parameters (such as weights and biases) are altered to optimize the associations between inputs and outputs. Model explainability or interpretability can be explored at this stage to elucidate the relative importance of inputs. External validation is then performed to assess the generalizability and reproducibility of the model. Model fairness, or its equity in performance across sociodemographic factors, is critical for safe and effective implementation. Operationalizing a model requires attention to implementation science best practices. It is imperative that the performance of an established model is continually monitored in prospective deployment to safeguard against population drifts or data shifts that may result in deteriorating performance over time.

Fig. 3:. Key elements in algorithm fairness.

From: Artificial intelligence-enabled decision support in nephrology To ensure that the algorithms used in artificial intelligence (AI)-enabled decision support represents and serves all patients equitably, several potential sources of bias must be considered and mitigated at each phase of algorithm development and deployment. PROBAST, Prediction model Risk Of Bias ASsessment Tool.

Fig. 4:. A framework for building an AI-competent medical workforce.

From: Artificial intelligence-enabled decision support in nephrology Didactic and practical training activities apply at various stages of pre-medical, medical, graduate and continuing medical education, building on previous stages of development. Theoretical and practical exposure to applications of artificial intelligence (AI) in healthcare has the potential to develop and sustain an AI-competent medical workforce.