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  • Review Article
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Artificial intelligence-enabled decision support in nephrology

Nature Reviews Nephrology volume 18, pages 452–465 (2022)Cite this article

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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.

Key points

  • 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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Fig. 1: Artificial intelligence systems through the lens of a learning healthcare system.
Fig. 2: Lifecycle of artificial intelligence for decision support.
Fig. 3: Key elements in algorithm fairness.
Fig. 4: A framework for building an AI-competent medical workforce.

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Acknowledgements

T.J.L. was supported by the National Institute of General Medical Sciences (NIGMS) of the NIH under Award Number K23 GM140268. T.O.-B. was supported by grants K01 DK120784, R01 DK123078 and R01 DK121730 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), grant R01 GM110240 from NIGMS, grant R01 EB029699 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and grant R01 NS120924 from the National Institute of Neurological Disorders and Stroke (NINDS). B.S.G. was supported by grant R01MH121923 from the National Institute of Mental Health (NIMH), grants R01AG059319 and R01AG058469 from the National Institute for Aging (NIA) and grant 1R01HG011407-01Al from the National Human Genome Research Institute (NHGRI). G.N.N. is supported by grants R01 DK127139 from NIDDK and R01 HL155915 from the National Heart Lung and Blood Institute (NHLBI). L.C. was supported by grant K23 DK124645 from the NIDDK. A.B. was supported by grant R01 GM110240 from NIGMS, grants R01 EB029699 and R21 EB027344 from NIBIB, grant R01 NS120924 from NINDS and by grant R01 DK121730 from NIDDK. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Authors and Affiliations

  1. Department of Surgery, University of Florida Health, Gainesville, FL, USA

    Tyler J. Loftus

  2. Department of Medicine, University of Florida Health, Gainesville, FL, USA

    Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Yuanfang Ren & Azra Bihorac

  3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Benjamin S. Glicksberg

  4. Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Benjamin S. Glicksberg

  5. Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA

    Jie Cao

  6. Department of Learning Health Sciences and Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA

    Karandeep Singh

  7. The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Lili Chan & Girish N. Nadkarni

  8. Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Lili Chan

  9. Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Lili Chan

  10. The Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Girish N. Nadkarni

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  1. Tyler J. Loftus
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Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Azra Bihorac.

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Competing interests

B.S.G. has received consulting fees from Anthem AI and consulting and advisory fees from Prometheus Biosciences. K.S. has received grant funding from Blue Cross Blue Shield of Michigan and Teva Pharmaceuticals for unrelated work, and serves on a scientific advisory board for Flatiron Health. G.N.N. has received consulting fees from AstraZeneca, Reata, BioVie, Siemens Healthineers and GLG Consulting; grant funding from Goldfinch Bio and Renalytix; financial compensation as a scientific board member and adviser to Renalytix; owns equity in Renalytix and Pensieve Health as a cofounder and is on the advisory board of Neurona Health. The other authors declare no competing interests.

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Related links

Cassandra: https://cassandra.apache.org/_/index.html

FAIR principles: https://www.go-fair.org/fair-principles/

National Patient-Centered Clinical Research Network: https://pcornet.org

Observational Medical Outcomes Partnership: https://www.ohdsi.org/

Spark Streaming: https://spark.apache.org/streaming/

What-If Tool: https://pair-code.github.io/what-if-tool/

Glossary

Nodes

Computational units in a neural network. Each node has a weight that is influenced by other nodes and affects predictions made by the neural network.

Computer vision

An artificial intelligence subfield in which deep models use pixels from images and videos as inputs.

Convolutional neural networks

(CNNs). A CNN is a type of neural network that assembles patterns of increasing complexity to avoid overfitting (fitting too closely on inputs), which can compromise predictive performance when the model is applied to new, previously unseen data. CNNs are frequently used in imaging applications.

Loss function

A mathematical function that calculates errors. Artificial intelligence algorithms are typically designed to minimize loss as the algorithm learns associations between input variables and outcomes.

Deep neural networks

Neural networks with several layers of nodes between the input layer and final output layer.

Generative adversarial neural networks

Two neural networks that compete with and learn from one another, offering the ability to generate synthetic data.

Hierarchical clustering

An algorithm that forms groups of elements that are similar to one another and different from others by iteratively merging points according to pairwise distances.

Random forest

A type of artificial intelligence model that assembles outputs from a set of decision trees and uses the majority vote or average prediction of the individual trees to produce a final prediction.

Gradient boosting

An artificial intelligence (AI) technique for iteratively improving predictive performance by ensuring that the next permutation of the AI model, when combined with the prior permutation, offers a performance improvement.

Sensitivity

The true positive rate; the percentage of patients with a disease for whom a model or test predicted a positive result, also known as recall. Sensitivity indicates the ability of a model or test to identify subjects who have a condition.

Specificity

The true negative rate; the percentage of patients without a disease for whom a model or test predicted a negative result. Specificity indicates the ability of a model or test to identify subjects who do not have a condition.

Positive predictive value

The probability that a positive prediction made by a model or test is correct according to the gold standard or ground truth. Positive predictive value is also known as precision.

Negative predictive value

The probability that a negative prediction made by a model or test is correct according to the gold standard or ground truth.

k-nearest-neighbours

A classification algorithm that makes predictions based on feature-space similarity to k nearby labelled instances, where k is the number of neighbours to consider and is assigned by the investigator.

Residual neural network (ResNet) CNN architecture

A form of convolutional neural network that uses skip connections or shortcuts to jump over some layers, which simplifies the network and accelerates learning.

F1 score

A measurement of accuracy that considers both precision, which is also known as positive predictive value, and recall, which is also known as sensitivity.

Convolutional autoencoders

Convolutional neural network variants that learn which filters should be used to detect features of interest among model inputs, which are usually imaging data.

Topic modelling

An artificial intelligence technique for detecting groups of text data that are similar to one another and different than others.

Ensemble model

A model that assembles outputs from multiple algorithms to achieve predictive performance that is greater than that of individual algorithms.

FAIR principles

The findability, accessibility, interoperability and reuse (FAIR) principles of digital assets for scientific investigation are intended to optimize the reuse of data.

Prediction model Risk Of Bias ASsessment Tool

(PROBAST). An instrument for assessing the risk of bias associated with a prediction model that provides diagnostic or prognostic information.

Observational Medical Outcomes Partnership

(OMOP). An organization that designed a common data model that standardizes the way medical information is captured across healthcare institutions and provides metadata tables describing relationships among data elements.

National Patient-Centered Clinical Research Network

(PCORnet). An organization that designed a common data model that standardizes the way medical information is captured across healthcare institutions and is widely adopted by institutions participating in the Patient Centered Outcomes Research Institute.

Predictive Model Markup Language

A programming language that standardizes methods for describing predictions models, which may facilitate sharing models among investigator groups.

Federated learning

A technique for generating a central artificial intelligence (AI) model that is built with information from several local AI models that train on local data. This approach has the potential advantage of training AI models on data from multiple centres without sharing data across centres, thereby promoting data security and privacy.

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Loftus, T.J., Shickel, B., Ozrazgat-Baslanti, T. et al. Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol 18, 452–465 (2022). https://doi.org/10.1038/s41581-022-00562-3

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