INTRODUCTION
Diabetes, one of the most prevalent chronic conditions worldwide, poses an escalating challenge to public health systems. As technological innovations continue to reshape healthcare, Artificial Intelligence (AI) emerges as a promising tool to enhance diabetes management by improving accuracy, personalization, and efficiency in clinical decision-making. However, while AI offers transformative potential, its successful integration into diabetes care requires a deep understanding of both systemic and frontline perspectives. This study sets out to explore the perspectives of healthcare professionals—those at the heart of patient care—by identifying the barriers and facilitators affecting AI implementation. In doing so, it aligns technological advancement with real-world clinical needs, advocating for a bottom-up approach in future AI program development.
RESEARCH METHODOLOGY AND SCOPING APPROACH
This study employed a rigorous scoping review methodology guided by PRISMA-ScR standards and was registered with the Open Science Framework (OSF) for transparency. Literature was selected through a two-phase process: initial screening of titles and abstracts, followed by a detailed full-text review. Three independent reviewers ensured objectivity, with a fourth resolving any conflicts. The Joanna Briggs Institute (JBI) framework was used to extract and assess data, ensuring high methodological quality. The study synthesized both quantitative metrics and qualitative narratives, enabling a comprehensive understanding of AI implementation from the healthcare provider’s viewpoint.
BARRIERS TO AI ADOPTION IN DIABETES CARE
Multiple barriers hinder the adoption of AI in diabetes care, according to healthcare professionals. These include concerns over clinical accuracy, especially in dynamic, patient-specific scenarios, as well as high implementation and maintenance costs. Data privacy and cybersecurity risks further complicate trust in AI systems. Transparency in AI-driven decision-making remains a challenge, creating hesitation among practitioners who seek accountability and clarity. Moreover, limited access to AI training prevents many healthcare workers from fully engaging with or benefiting from these systems. These challenges emphasize the need for systemic and educational interventions before large-scale AI adoption.
FACILITATORS THAT ENCOURAGE AI INTEGRATION
Despite the barriers, several key facilitators promote the integration of AI in diabetes management. Healthcare workers recognize AI’s potential to enhance clinical efficiency by reducing workload, improving diagnostic accuracy, and saving time in routine processes. The ease of use and growing user-friendliness of AI interfaces also support broader adoption. Importantly, organizational support—such as investment in infrastructure, training, and interdisciplinary collaboration—significantly boosts trust in these technologies. These facilitators highlight that with the right support structures, AI can be seamlessly integrated into existing healthcare workflows, particularly in chronic disease management.
HEALTHCARE WORKERS’ ROLE IN BOTTOM-UP AI DEVELOPMENT
A key insight from this study is the pivotal role healthcare professionals play in shaping AI applications. Rather than imposing top-down systems, successful AI integration demands a bottom-up approach that values frontline insights and experiences. Healthcare workers can identify practical gaps, suggest realistic functionalities, and provide feedback for continuous system improvement. Their active participation not only increases system usability but also fosters a sense of ownership, trust, and accountability. This participatory approach aligns with sustainable innovation and ensures that AI systems are built to meet real clinical challenges.
IMPLICATIONS FOR PUBLIC HEALTH AND FUTURE RESEARCH
The findings carry profound implications for public health policy and AI research. Integrating AI in diabetes care is not merely a technical task but a socio-organizational transformation. Future research should focus on long-term evaluations of AI tools in real-world settings, paying attention to their impact on patient outcomes, clinician workflows, and equity in healthcare delivery. Furthermore, policy frameworks must prioritize training programs, ethical AI standards, and data governance. Aligning AI innovation with public health goals ensures not only technological progress but also socially responsible and equitable healthcare evolution.
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