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Diabetes: Management and Pharmacotherapy01:15

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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Diabetes Mellitus: Overview and Type I Subtype01:22

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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Insulin: Dosing Regimen and Adverse Effects01:16

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Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
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Methodology for Safe and Secure AI in Diabetes Management.

Remco Jan Geukes Foppen1, Vincenzo Gioia2, Shreya Gupta3

  • 1Independent, Anzio, Italy.

Journal of Diabetes Science and Technology
|December 27, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in diabetes management offers personalized care but faces safety and security challenges. Explainable AI (xAI) is crucial for secure, compliant, and trustworthy AI-driven diabetes solutions.

Keywords:
AI in health careartificial intelligencecybersecuritydataexplainable AIhealth information

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Diabetes Technology

Background:

  • Artificial intelligence (AI) shows promise for enhancing diabetes management through improved monitoring and personalized therapies.
  • Clinical integration of AI in diabetes care presents significant challenges concerning patient data safety, security, and regulatory compliance.
  • Potential direct impacts on patient health necessitate careful consideration of AI system design and implementation.

Purpose of the Study:

  • To provide guidance for developers and researchers on addressing safety, security, and compliance challenges in AI systems for diabetes management.
  • To highlight the critical role of explainable AI (xAI) in ensuring security, compliance, and user trust for AI-driven diabetes solutions.
  • To examine both technical and regulatory aspects crucial for developing trustworthy and effective explainable AI applications in diabetes care.

Main Methods:

  • Analyzing the AI system lifecycle to integrate xAI frameworks, security measures, and risk mitigation strategies.
  • Examining technical methodologies for constructing xAI systems throughout the AI development process.
  • Reviewing regulatory frameworks, including Governance, Risk, and Compliance (GRC) standards from bodies like the FDA, for AI-enabled healthcare applications.

Main Results:

  • Understanding the AI system lifecycle is key to building robust xAI frameworks that address security and mitigate risks.
  • Regulatory analysis identifies essential GRC standards for ensuring the safety, efficacy, and ethical integrity of AI in diabetes care.
  • Integrating technical and regulatory insights provides a pathway for developing trustworthy AI solutions.

Conclusions:

  • Explainable AI (xAI) is foundational for secure, compliant, and trustworthy AI in diabetes management, enhancing clinical decision-making.
  • Addressing both technical development and regulatory requirements is essential for safe and effective AI-enabled diabetes care.
  • Actionable insights are provided to foster patient engagement and improve clinical outcomes through reliable AI solutions.