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Related Concept Videos

Insulin: Dosing Regimen and Adverse Effects01:16

Insulin: Dosing Regimen and Adverse Effects

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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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Insulin Formulations: Types and Delivery01:27

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Insulin preparations are categorized by their duration of action into short-acting and long-acting types. Two strategies are used to modify insulin's absorption and pharmacokinetic profile: slowing the absorption post-subcutaneous injection, or altering human insulin's amino acid sequence or protein structure. These changes retain the insulin's ability to bind to the insulin receptor, but alter its behavior in solution or after injection.
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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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Obesity01:24

Obesity

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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Related Experiment Video

Updated: Sep 13, 2025

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health
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"Digital Clinicians" Performing Obesity Medication Self-Injection Education: Feasibility Randomized Controlled Trial.

Sean Coleman1,2, Caitríona Lynch1, Hemendra Worlikar2

  • 1Department of Diabetes, Endocrinology, and Metabolism, University Hospital Galway, Newcastle Rd, Galway, H91 YR71, Ireland, 353 851604880.

JMIR Diabetes
|July 30, 2025
PubMed
Summary

Artificial intelligence (AI) digital clinicians improved patient knowledge on semaglutide injections but were less trusted than human nurses. These AI tools show potential for healthcare education and resource redistribution in obesity treatment.

Keywords:
AIMLNLPRCTsartificial intelligenceautomateautomationchatGPTchatbotsclinical educationdeep learningdigital clinicianfeasibility studieslarge language modelsmachine learningmachine-human interfacemedicationnatural language processingobesityrandomized controlled trialstrustvirtual human

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Patient Education

Background:

  • The global obesity epidemic and rise of injectable treatments strain healthcare resources.
  • Artificial intelligence (AI) chatbots demonstrate capabilities in clinical tasks and patient support.
  • There is a need for innovative solutions to deliver patient education for new obesity medications.

Purpose of the Study:

  • To evaluate the efficacy of a "digital clinician" (AI chatbot with a digital avatar) for patient education on semaglutide injections.
  • To compare knowledge acquisition, self-efficacy, satisfaction, and trust between AI-driven and conventional nursing education.

Main Methods:

  • A randomized controlled trial compared a "digital clinician" avatar with standard nursing education for semaglutide initiation.
  • Key outcomes included knowledge test scores, self-efficacy, consultation satisfaction, and trust.
  • Participants were assessed immediately post-intervention and at a 2-week follow-up.

Main Results:

  • Patients educated by the "digital clinician" showed significantly higher post-consultation knowledge.
  • However, patients receiving conventional education reported higher satisfaction and trust.
  • No significant difference in self-efficacy was observed between groups.

Conclusions:

  • AI-powered "digital clinicians" can effectively deliver healthcare education, enhancing knowledge transfer.
  • Human healthcare providers remain more trusted than AI avatars for patient education.
  • "Digital clinicians" may help optimize healthcare resource allocation in bariatric services.