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Ethical Dilemmas II01:30

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Resolving an ethical dilemma in healthcare involves a systematic approach that considers every aspect of the issue, respecting both the patient's needs and values and the healthcare professional's ethical obligations. Here are potential steps to resolve an ethical dilemma:
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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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Updated: Feb 20, 2026

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Algor-Ethics in Diabetes Care: Mapping the Route.

Joshua Bemporad1, Francesco De Domenico1, Paolo Pozzilli1,2,3

  • 1Campus Bio-Medico University, Rome, Italy.

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Summary

Artificial intelligence (AI) offers advanced tools for diabetes management, but ethical considerations are crucial. Algorithmic decision-making in healthcare requires careful oversight to ensure patient safety and trust.

Keywords:
Algor‐ethicsalgorethicsartificial intelligencediabetesdiabetes careethicsmachine learning

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Area of Science:

  • Digital Health Technologies
  • Medical Informatics
  • Bioethics

Background:

  • Diabetes mellitus is a complex global health issue, often linked with obesity and cardiovascular diseases.
  • Effective diabetes management relies on integrating diverse patient data for personalized treatment.
  • Artificial intelligence (AI) and algorithmic systems are increasingly used to aid clinical decisions in diabetes care.

Purpose of the Study:

  • To explore the foundational concepts of Algor-ethics in diabetes care.
  • To analyze the current integration of AI in managing diabetes.
  • To highlight the ethical and epistemic implications of AI-driven decision-making in complex diabetes cases.

Main Methods:

  • Literature review on AI in diabetes management.
  • Analysis of ethical frameworks for algorithmic decision-making.
  • Exploration of Algor-ethics principles applied to patient care.

Main Results:

  • AI integration in diabetes care presents both opportunities for improved outcomes and significant ethical challenges.
  • Algorithmic decision-making may lack transparency and oversight, impacting patient autonomy and trust.
  • The need for responsible AI implementation is highlighted, especially for vulnerable patient populations.

Conclusions:

  • A framework for safe, equitable, and responsible AI implementation in diabetes care is essential.
  • Addressing the ethical and epistemic challenges of AI is critical for building trust in digital health solutions.
  • Algor-ethics provides a vital lens for navigating the complexities of AI in clinical practice.