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Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

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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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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A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

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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...
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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Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.

Charat Thongprayoon1, Francesco Pesce2,3, Wisit Cheungpasitporn1

  • 1Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.

Clinics and Practice
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PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing rare disease diagnosis and care by integrating diverse data. This review explores AI applications in rare kidney diseases and proposes a framework for robust, deployable systems.

Keywords:
artificial intelligenceclinical decision supportgenomic diagnosisnephrologyrare diseases

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Area of Science:

  • Medical Informatics
  • Genomics
  • Artificial Intelligence

Background:

  • Rare diseases impact over 300 million globally, facing challenges in diagnosis and characterization due to data fragmentation and heterogeneity.
  • Artificial intelligence (AI) offers solutions by integrating genomics, imaging, electronic health records, and patient-generated data.

Purpose of the Study:

  • To review current AI applications across the rare disease continuum.
  • To propose a framework for evaluating AI models for clinical deployment.
  • To highlight advancements and persistent challenges in AI for rare diseases.

Main Methods:

  • Synthesis of current literature on AI applications in rare disease diagnosis, phenotyping, prognosis, and therapy.
  • Analysis of AI tools in kidney care, including variant prioritization and risk stratification.
  • Development of a framework to differentiate exploratory AI models from clinically deployable systems.

Main Results:

  • AI enables integration of multi-modal data for improved rare disease management.
  • Specific AI tools are emerging for genomic variant prioritization and risk stratification in rare kidney diseases.
  • Challenges remain in validation, generalizability, equity, and workflow integration.

Conclusions:

  • AI holds significant promise for advancing rare disease care, particularly in kidney diseases.
  • A robust framework is needed to guide the clinical implementation of AI tools.
  • Future directions include federated learning, digital twins, and AI-driven clinical decision agents for precision care.