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Artificial intelligence in 2023

The dawn of multimodal artificial intelligence in nephrology

Nature Reviews Nephrology volume 20, pages 79–80 (2024)Cite this article

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The next generation of artificial intelligence (AI)-enabled nephrology will leverage generalist models that link diverse multimodal patient data with the linguistic and emergent capabilities of large language models. In 2023, advances in AI that linked novel unstructured data with physiological and clinical characteristics moved the field closer to realizing this vision.

Key advances

  • The launch of ChatGPT1 was an important step in the democratization of artificial intelligence (AI); large language models will have a critical role in shaping future research and AI-augmented clinical practice in nephrology.

  • An automated quantitative histopathological morphometry approach, termed pathomics, reliably predicted the progression of kidney diseases8; this technology represents an important step towards precision medicine.

  • An AI system reliably estimated biomarkers of systemic diseases, including estimated glomerular filtration rate, from external retinal photographs9; this approach offers new opportunities for noninvasive, inexpensive and scalable screening for kidney disease in low-resource settings.

  • A deep learning analysis of blood-flow sounds detected arteriovenous fistula stenoses in patients on haemodialysis with comparable performance to an expert nephrologist performing a physical examination10; this approach has the potential for patient-facing applications.

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Fig. 1: Overview of medical foundation models for augmented intelligence in nephrology.

References

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Acknowledgements

The authors’ work was supported by the National Institute of General Medical Sciences (grant number R01 GM110240).

Author information

Authors and Affiliations

  1. Division of Nephrology, Hypertension and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL, USA

    Benjamin Shickel & Azra Bihorac

  2. Intelligent Clinical Care Center (IC3), University of Florida, Gainesville, FL, USA

    Benjamin Shickel & Azra Bihorac

Authors
  1. Benjamin Shickel
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  2. Azra Bihorac
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Correspondence to Azra Bihorac.

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The authors declare no competing interests.

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Shickel, B., Bihorac, A. The dawn of multimodal artificial intelligence in nephrology. Nat Rev Nephrol 20, 79–80 (2024). https://doi.org/10.1038/s41581-023-00799-6

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