Cangelosi Giovanni, Conti Andrea, Caggianelli Gabriele, Panella Massimiliano, Petrelli Fabio, Mancin Stefano, Ratti Matteo, Masini Alice
School of Pharmacy, Experimental Medicine and "Stefani Scuri" Public Health Department, University of Camerino, 62032 Camerino, Italy.
Department of Translational Medicine, Università del Piemonte Orientale, 28100 Novara, Italy.
Medicina (Kaunas). 2025 Aug 1;61(8):1403. doi: 10.3390/medicina61081403.
Diabetes is a global public health challenge, with increasing prevalence worldwide. The implementation of artificial intelligence (AI) in the management of this condition offers potential benefits in improving healthcare outcomes. This study primarily investigates the barriers and facilitators perceived by healthcare professionals in the adoption of AI. Secondarily, by analyzing both quantitative and qualitative data collected, it aims to support the potential development of AI-based programs for diabetes management, with particular focus on a possible bottom-up approach. A scoping review was conducted following PRISMA-ScR guidelines for reporting and registered in the Open Science Framework (OSF) database. The study selection process was conducted in two phases-title/abstract screening and full-text review-independently by three researchers, with a fourth resolving conflicts. Data were extracted and assessed using Joanna Briggs Institute (JBI) tools. The included studies were synthesized narratively, combining both quantitative and qualitative analyses to ensure methodological rigor and contextual depth. : The adoption of AI tools in diabetes management is influenced by several barriers, including perceived unsatisfactory clinical performance, high costs, issues related to data security and decision-making transparency, as well as limited training among healthcare workers. Key facilitators include improved clinical efficiency, ease of use, time-saving, and organizational support, which contribute to broader acceptance of the technology. : The active and continuous involvement of healthcare workers represents a valuable opportunity to develop more effective, reliable, and well-integrated AI solutions in clinical practice. Our findings emphasize the importance of a bottom-up approach and highlight how adequate training and organizational support can help overcome existing barriers, promoting sustainable and equitable innovation aligned with public health priorities.
糖尿病是一项全球性的公共卫生挑战，在全球范围内的患病率不断上升。在这种疾病的管理中实施人工智能（AI）有望改善医疗保健结果。本研究主要调查医疗保健专业人员在采用人工智能时所察觉到的障碍和促进因素。其次，通过分析收集到的定量和定性数据，旨在支持基于人工智能的糖尿病管理项目的潜在发展，特别关注一种可能的自下而上的方法。按照PRISMA-ScR报告指南进行了一项范围综述，并在开放科学框架（OSF）数据库中进行了注册。研究选择过程分两个阶段进行——标题/摘要筛选和全文评审——由三名研究人员独立进行，第四名研究人员解决冲突。使用乔安娜·布里格斯研究所（JBI）工具提取和评估数据。对纳入的研究进行了叙述性综合，结合定量和定性分析，以确保方法的严谨性和背景深度。：糖尿病管理中人工智能工具的采用受到多种障碍的影响，包括临床性能不尽人意、成本高昂、与数据安全和决策透明度相关的问题，以及医护人员培训有限。关键的促进因素包括提高临床效率、易用性、节省时间和组织支持，这些因素有助于更广泛地接受该技术。：医护人员的积极持续参与为在临床实践中开发更有效、可靠且整合良好的人工智能解决方案提供了宝贵机会。我们的研究结果强调了自下而上方法的重要性，并突出了充分的培训和组织支持如何有助于克服现有障碍，促进与公共卫生优先事项相一致的可持续和公平创新。
