{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Health-care leaders' perspectives on AI implementation in Uganda: overcoming barriers, driving innovation and strategic considerations.","author":[{"@type":"Person","name":"Mahadih Kyambade"},{"@type":"Person","name":"Afulah Namatovu"}],"datePublished":"2025","isPartOf":{"@type":"Periodical","name":"Leadership in health services (Bradford, England)"},"identifier":"https://doi.org/10.1108/LHS-02-2025-0025","description":"\n Purpose\n The implementation of artificial intelligence (AI) in health care presents significant opportunities for improving efficiency, decision-making and patient outcomes. However, health-care leaders often face resistance and multiple challenges in adopting AI technologies, leading to slow and inconsistent implementation. This study aims to explore the perspectives of health-care leaders in Uganda regarding AI adoption, focusing on barriers, innovation drivers and strategic considerations necessary for effective AI integration.\n \n \n Design/methodology/approach\n The study used a qualitative, exploratory approach using semi-structured interviews with 24 leaders from various public health-care institutions in Uganda. Data collection took place from December 2023 to February 2024. The analysis was conducted using qualitative content analysis with an inductive approach to identify key themes related to AI implementation challenges and strategies.\n \n \n Findings\n The study identified three main categories of challenges affecting AI implementation in Uganda’s health-care system: External Constraints, including regulatory gaps, limited funding and infrastructure deficits; Institutional Capacity for Change Management, highlighting resistance to change, lack of technical expertise and inadequate leadership support; and Transformation of health-care practices, which includes concerns about AI’s impact on job roles, ethical considerations and data security. Despite these challenges, health-care leaders acknowledged AI’s potential to enhance service delivery, improve diagnostic accuracy and optimize health-care workflows.\n \n \n Practical implications\n The findings underscore the need for targeted implementation strategies, including investment in AI education and training for health-care professionals, the development of clear policies and regulatory frameworks and fostering collaboration between health-care institutions, policymakers and technology providers. Strengthening leadership capacity in change management and ensuring ethical AI deployment are crucial for successful adoption.\n \n \n Originality/value\n This study contributes to the limited body of research on AI adoption from the perspective of health-care leaders in developing countries, particularly in Uganda. Unlike previous studies that focus on general AI acceptance, this research provides a leadership-centric analysis of the barriers and strategic approaches necessary for AI implementation. The insights generated can inform policymakers, health-care administrators and technology developers on designing more effective AI adoption frameworks tailored to resource-constrained settings.\n ","url":"https://innscience.ca/en/community/literature/985db8c6-c9e2-477e-a4a3-c3e582180c12"}