ARTIFICIAL INTELLIGENCE IN RENAL HEALTHCARE: CURRENT TRENDS AND FUTURE PROSPECTS

Reference

1. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–1318.
2. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
3. Tangri N, Grams ME, Levey AS, et al. Multinational assessment of accuracy of CKD prognosis equations. JAMA. 2016;315(2):164–174.
4. Flechet M, Güiza F, Schetz M, et al. AKI prediction using data-driven models in critically ill patients. Clin J Am Soc Nephrol. 2017;12(3):399–408.
5. Goldstein BA, Navar AM, Pencina MJ, Ioannidis JPA. Opportunities and challenges in developing risk prediction models with electronic health records data. J Am Med Inform Assoc. 2017;24(1):198–208.
6. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1350.
7. Chen JH, Asch SM. Machine learning and prediction in medicine. N Engl J Med. 2017;376(26):2507–2509.
8. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–2664.
9. Bae S, Oh TR, Kim CS, et al. Machine learning algorithms for predicting progression of chronic kidney disease. Sci Rep. 2020;10(1):19264–19273.
10. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–36.
11. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–510.
12. Liew C. The future of radiology augmented with artificial intelligence. Radiol Clin North Am. 2018;56(2):263–273.
13. Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320(21):2199–2200.
14. Tomašev N, Glorot X, Rae JW, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116–119.
15. Ravizza S, Huschto T, Adamov A, et al. Predicting the early risk of chronic kidney disease. PLOS One. 2020;15(1):227231–227245.
16. Kwon JM, Kim KH, Jeon KH, et al. Artificial intelligence algorithm for predicting progression to end-stage renal disease. Kidney Res Clin Pract. 2021;40(3):396–405.
17. Marsh JN, Matlock MK, Kudose S, et al. Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE Trans Med Imaging. 2018;37(12):2718–2728.
18. Hermsen M, de Bel T, den Boer M, et al. Deep learning-based histopathologic assessment of kidney tissue. J Am Soc Nephrol. 2019;30(10):1968–1979.
19. Kolachalama VB, Singh P, Lin CQ, et al. Association of pathological fibrosis with renal survival using deep neural networks. Kidney Int Rep. 2018;3(2):464–475.
20. Bueno G, Fernandez-Carrobles MM, Deniz O, et al. Deep learning analysis of histopathological images of kidney tissue. Comput Med Imaging Graph. 2020;80:101688–101697.
21. Barbieri C, Molina M, Ponce P, et al. Artificial intelligence in dialysis: predictive models for intradialytic hypotension. Clin Kidney J. 2020;13(3):442–450.
22. Yu J, Chen L, Zhang W, et al. Artificial intelligence for personalized hemodialysis treatment. Artif Intell Med. 2021;113:102025–102033.
23. Sakurai K, Tominaga Y, et al. Machine learning-based prediction of dialysis complications. Ren Replace Ther. 2020;6(1):32–39.
24. Kers J, Peters-Sengers H, Heemskerk MBA, et al. Prediction models for delayed graft function using machine learning. Kidney Int. 2021;99(5):1232–1241.
25. Yoo KD, Noh J, Lee H, et al. A machine learning approach for predicting graft survival. Transplant Proc. 2020;52(7):2091–2098.
26. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038–1040.
27. Chen M, Zhang Y, Wang X, et al. Artificial intelligence in drug discovery for kidney diseases. Drug Discov Today. 2021;26(10):2389–2397.
28. Van Norman GA. Drugs, devices, and the FDA: Part 2. JACC Basic Transl Sci. 2016;1(4):277–287.
29. Legrand M, Mebazaa A. Artificial intelligence and big data in nephrology. Nat Rev Nephrol. 2022;18(4):211–213.
30. Matheny ME, Whicher D, Thadaney Israni S. Artificial intelligence in health care. Nat Biomed Eng. 2020;4(10):863–865.
31. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with AI. BMC Med. 2019;17(1):195–205.
32. Topol EJ. Preparing the healthcare workforce to deliver the digital future. Lancet. 2019;394(10196):117–122.
33. Van Norman GA. Drugs, devices, and the FDA: Part 2. JACC Basic Transl Sci. 2016;1(4):277–280
34. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358.
35. Thottakkara P, Ozrazgat-Baslanti T, Hupf BB, Rashidi P, Pardalos P, Momcilovic P, et al. Application of machine learning techniques to high-dimensional clinical data to predict AKI. J Crit Care. 2021;31(1):50-56.