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Related Concept Videos

Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

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Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
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Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

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Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
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Acute Kidney Injury I: Introduction01:22

Acute Kidney Injury I: Introduction

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Introduction:Acute Kidney Injury (AKI) describes a swift decrease in kidney function occurring over hours to days, characterized by the kidneys' failure to remove waste products from the bloodstream. This leads to dangerous complications like metabolic acidosis, fluid overload, and electrolyte imbalances, such as hyperkalemia, which can cause life-threatening arrhythmias. AKI is common in both hospital and outpatient settings, often triggered by dehydration, sepsis, or exposure to nephrotoxic...
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Acute Kidney Injury V: Interprofessional Care01:20

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Acute Kidney Injury (AKI) requires a collaborative healthcare approach to restore renal function and prevent complications. Essential management strategies involve monitoring fluid and electrolyte balance, adjusting medications, initiating dialysis when necessary, and providing nutritional support.Fluid and Electrolyte ManagementFluid Monitoring: Regularly monitoring body weight, central venous pressure, and urine output helps detect fluid imbalances early. Patient intake and output are...
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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Chronic Kidney Disease I: Introduction01:25

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Chronic Kidney Disease (CKD) arises when the kidneys progressively lose their ability to function, ultimately leading to end-stage renal disease. At this advanced stage, the kidneys can no longer filter waste or maintain essential body functions, requiring renal replacement therapy (RRT) through dialysis or a kidney transplant for survival.Early-stage chronic kidney disease and detection challengesIn CKD's early stages, symptoms often remain absent because healthy nephrons compensate for...
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Artificial Intelligence in Nephrology: Pioneering Precision with Multimodal Intelligence.

Pushkala Jayaraman1, Ishita Vasudev2, Akinchan Bhardwaj3

  • 1The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine Mount Sinai, New York, USA.

Indian Journal of Nephrology
|September 2, 2025
PubMed
Summary

Artificial intelligence (AI) offers significant advancements in nephrology for early kidney disease detection and personalized treatment. Challenges remain in data integration and ethical standards for AI deployment in kidney care.

Keywords:
AlgorithmsArtificial intelligenceCritical careGPT-4Machine learningPredictive models

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Area of Science:

  • Nephrology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is rapidly advancing in healthcare, with significant potential in nephrology.
  • AI tools, including large language models like GPT-3 and GPT-4, show promise in medical education and diagnostics.
  • AI's capacity to analyze complex datasets from electronic health records, imaging, and genetics can aid early detection and personalized treatment planning.

Purpose of the Study:

  • To explore the role and applications of artificial intelligence in nephrology.
  • To review AI's diagnostic capabilities, outcome prediction, and treatment planning in kidney care.
  • To highlight recent studies on AI's potential and limitations in kidney disease management.

Main Methods:

  • Review of AI applications in nephrology, including predictive models and non-invasive diagnostics.
  • Analysis of AI's ability to process diverse data modalities (EHR, imaging, genetics).
  • Discussion of AI-driven tools for chronic kidney disease and acute kidney injury prediction.

Main Results:

  • AI models demonstrate accuracy in clinical assessments and can predict risk factors for kidney diseases.
  • Non-invasive diagnostics, such as retinal imaging, are enhanced by AI for early biomarker detection.
  • AI facilitates personalized treatment planning and clinical decision-making through complex data analysis.

Conclusions:

  • AI presents a promising, cost-effective approach for early kidney disease detection and intervention.
  • Successful AI deployment in nephrology requires addressing data integration, model generalizability, and ethical considerations.
  • Transparency, explainability, and patient trust are crucial for integrating AI into clinical kidney care.