Related Experiment Video
Updated: Sep 15, 2025

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
Published on: April 12, 2021
Integrating artificial intelligence in community-based diabetes care programmes: enhancing inclusiveness, diversity,
Samah Hassan1,2,3, Sarah Ibrahim4,2,5, Joanna Bielecki3,6,7
1Program for Health System and Technology Evaluation, University Health Network, Toronto, Ontario, Canada sam.hassan@mail.utoronto.ca.
Introduction:
Marginalised populations-such as racialised groups, low-income individuals, newcomers and those in rural areas-disproportionately experience severe diabetes-related complications, including diabetic foot ulcers, retinopathy and amputations, due to systemic inequities and limited access to care. Although community-based programmes address cultural and accessibility barriers, their isolation from mainstream healthcare systems leads to fragmented care and missed opportunities for early intervention.Artificial intelligence (AI)-powered technologies can enhance accessibility and personalisation, particularly for underserved populations. However, integrating AI into community settings remains underexplored, with socioethical concerns around inclusion, diversity, equity and accessibility requiring urgent attention.This realist review aims to examine how, why and under what circumstances AI applications can be effectively integrated into community-based diabetic care for marginalised populations. The review will develop a programme theory to guide ethical, inclusive and effective AI implementation to ensure AI-driven innovations address health disparities and promote culturally sensitive, accessible care for all.
Methods And Analysis:
Using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) extension for Reviews guidelines, this realist review will systematically search MEDLINE, Embase, CINAHL, Cochrane library, Google Scholar and Scopus, alongside grey literature. A two-stage screening process will identify eligible studies, and data extraction will use a developed tool. Synthesis will employ realist logic, analysing relationships between contexts (eg, organisational capacity), mechanisms (eg, AI functionalities) and outcomes (eg, reduced disparities).
Ethics And Dissemination:
Ethics approval is not required for conducting this realist review. Ethics approval will be obtained from the University of Toronto; however, following the completion of the realist review for patients and community members' engagement to support knowledge mobilisation and dissemination to ensure practical application and reciprocity.
Prospero Registration Number:
This protocol was registered at PROSPERO (CRD42025636284).
Related Concept Videos
Community Based Intervention
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...
Integrated Healthcare System
Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Diabetes Mellitus: Overview and Type I Subtype
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
Diabetes: Management and Pharmacotherapy
Insulin remains the cornerstone of treatment for most patients with type 1 and many...
Interdisciplinary Care: The Health Care Team-I
Physicians
The physician's primary responsibility is to diagnose illness and direct the medical or surgical treatment of the condition. The authority to admit patients to a healthcare agency or institution and practice care within that setting is granted to physicians by the healthcare agency or institution...

