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Generative artificial intelligence: In the search for new landscapes in basic and clinical nephrology.

Farnoush Kiyanpour1,2, Ali Motahharynia2, Marek Ostaszewski3

  • 1Department of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of Research in Medical Sciences : the Official Journal of Isfahan University of Medical Sciences
|September 22, 2025
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Summary

Artificial intelligence (AI) offers powerful tools for analyzing complex data in chronic kidney disease research. AI applications in nephrology enhance diagnosis, prognosis, and drug discovery, though ethical considerations are paramount.

Keywords:
Artificial intelligencediabetic kidney diseasedrug repositioningdrug target identificationkidney diseases

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Area of Science:

  • Nephrology
  • Systems Biology
  • Artificial Intelligence

Background:

  • Systems biology models have advanced the understanding of complex diseases like chronic kidney disease (CKD).
  • Despite extensive omics data, translating findings into clinical applications for CKD remains difficult.
  • Artificial intelligence (AI), particularly generative AI, presents a promising avenue for data mining and integration in complex diseases.

Purpose of the Study:

  • To explore the potential of AI in addressing the challenges of data complexity and clinical translation in nephrology.
  • To highlight AI's role in improving diagnosis, prognosis, and therapeutic strategies for chronic kidney disease.
  • To discuss the implications of AI in precision nephrology and drug discovery.

Main Methods:

  • Leveraging generative AI for mining, integrating, and processing diverse and complex raw data.
  • Application of AI tools for enhanced diagnosis and prognosis in clinical nephrology.
  • Utilizing AI for identifying novel therapeutic targets and repurposing existing drugs.

Main Results:

  • AI tools demonstrate significant potential in improving diagnostic and prognostic accuracy in nephrology.
  • AI facilitates the identification of new therapeutic targets and drug repurposing opportunities for CKD.
  • AI enables advancements towards precision nephrology by personalizing patient care strategies.

Conclusions:

  • AI, especially generative AI, is a transformative technology for clinical nephrology and chronic kidney disease management.
  • The integration of AI promises to accelerate the translation of research findings into actionable clinical solutions.
  • The rapid advancement of AI in nephrology necessitates proactive consideration of associated ethical and legal concerns.