Literature

Artificial Intelligence and Machine Learning in Dialysis Ready for Prime Time?

Kotanko P, Zhang H, Wang Y
Clinical Journal of the American Society of Nephrology, 2023.
DOI: 10.2215/CJN.0000000000000089

Application of Natural Language Processing in Nephrology Research

Douglas F, Chan L.
Clinical Journal of the American Society of Nephrology, 2023.
DOI: 10.2215/CJN.0000000000000118

Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology

Tangri N, Cheungpasitporn W, Crittenden SD, Fornoni A, Peralta CA, Singh K, Usvyat LA, Waterman AD
Journal of the American Society of Nephrology, 2025.
DOI: 10.1681/ASN.0000000929

Home Dialysis Prediction Using Artificial Intelligence

Monaghan CK, Willetts J, Han H, Chaudhuri S, Ficociello LH, Kraus MA, 3 Giles HE, Usvyat L, Turk J
Kidney Medicine, 2025.
DOI: 10.1016/j.xkme.2024.100949

Artificial intelligence in kidney disease and dialysis: from data mining to clinical impact

Neri L, Zhang H, Usvyat LA.
Current Opinion in Nephrology and Hypertension, 2026.
DOI: 10.1097/MNH.0000000000001132

Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance

Van Calster B, Collins GS, Vickers AJ, Wynants L, Kerr KF, Barreñada L, Varoquaux G, Singh K, Moons KGM, Hernandez-Boussard T, Timmerman D, McLernon DJ, Smeden Mvan, Steyerberg EW
Lancet Digit Health, 2025.
DOI: /10.1016/j.landig.2025.100916

Prospective comparison of econometric, machine learning, and foundation models for forecasting emergency department boarding patients

Poursoltan L, Cao J, Clay B, Trimble B, Adrid L, Pan J, Chua A, Bell J, Longhurst CA, Zhu K, Singh K
npj Health Systems, 2025.
DOI: 10.1038/s44401-025-00054-z

Evaluation of electronic health record-integrated artificial intelligence chart review

Kahl NM, Frieden MJ, Pope ZR, Millen MM, Tolia VM, Chan TC, Longhurst CA, Singh K, You AX
npj Health Systems, 2026.
DOI: 10.1038/s44401-025-00064-x

Generalizability of an acute kidney injury prediction model across health systems

Cao J, Zhang X, Shahinian V, Yin H, Steffick D, Saran R, Crowley S, Mathis M, Nadkarni GN, Heung M, Singh K
Nature Machine Intelligence, 2022.
DOI: 10.1038/s42256-022-00563-8

Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification

Cama-Olivares A, Braun C, Takeuchi T, O’Hagan EC, Kaiser KA, Ghazi L, Chen J, Forni LG, Kane-Gill SL, Ostermann M, Shickel B, Ninan J, Neyra JA
Journal of the American Society of Nephrology, 2025.
DOI: 10.1681/ASN.0000000702

Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe AKI

Koyner JL, Martin J, Carey KA, Caskey J, Edelson DP, Mayampurath A, Dligach D, Afshar M, ChurpekMM
Clinical Journal of the American Society of Nephrology, 2025.
DOI: 10.2215/CJN.0000000695

From bytes to bites: application of large language models to enhance nutritional recommendations

Bergling K, Wang LC, Shivakumar O, Nandorine Ban A, Moore LW, Ginsberg N, Kooman J, Duncan N, Kotanko P, Zhang H
Clinical Kidney Journal, 2025.
DOI: 10.1093/ckj/sfaf082

Application of ChatGPT to Support Nutritional Recommendations for Dialysis Patients – A Qualitative and Quantitative Evaluation

Wang L, Zhang H, Ginsberg N, Ban AN, Kooman JP, Kotanko P
Journal of Renal Nutrition, 2024.
DOI: 10.1053/j.jrn.2024.09.001

Performance of GPT-4 Vision on kidney pathology exam questions

Miao J, Thongprayoon C, Cheungpasitporn W, Cornell LD
Am J Clin Pathol, 2024.
DOI: 10.1093/ajcp/aqae030

The dawn of multimodal artificial intelligence in nephrology

Shickel B, Bihorac A
Nature Reviews Nephrology, 2024.
DOI: 10.1038/s41581-023-00799-6

Imaging and spatially resolved mass spectrometry applications in nephrology

Gorman BL, Shafer CC, Ragi N, Sharma K, Neumann EK, Anderton CR
Nature Reviews Nephrology, 2025.
DOI: 10.1038/s41581-025-00946-1

Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

Meng Z, Guan Z, Yu S, Wu Y, Zhao Y, Shen J, et al.
The Lancet Digital Health, 2025.
DOI: 10.1016/j.landig.2025.02.008