Exploring the Feasibility, Benefits, and Risks of Integrating AI Across the Implementation Research Spectrum to Accelerate the Translation of Public Health Interventions

Background: Global public health crises like COVID-19 and MPOX underscore the need to accelerate the process of generating knowledge and translating it into healthcare practice. Despite advancements in implementation science (IS), gaps remain in translating evidence into practice, leading to delays, inequities, and sustainability challenges. Artificial Intelligence (AI) offers potential solutions by rapidly synthesizing research and developing tailored strategies to address barriers, improving healthcare delivery.Objective: This study aims to explore the feasibility, benefits, and risks of integrating AI into implementation science to enhance the adoption of evidence-based public health interventions. Key research questions include: (1) How do stakeholders perceive the feasibility and benefits of using AI to accelerate the translation of public health interventions into practice? (2) What are their views on the risks, ethical challenges, and equity implications of using AI in implementation research?Methods: This qualitative study will use semi-structured interviews to gather perspectives on integrating AI into IS. Participants will include implementation researchers, AI specialists, healthcare providers, policymakers, and patients, recruited through purposive and snowball sampling. Interviews will focus on feasibility, benefits, challenges, and equity implications.Significance: The study could enhance healthcare practices by exploring AI potential to improve the efficiency, equity, and sustainability of evidence-based interventions, particularly in resource-limited settings. Findings will inform the activities of the CIHR/PHAC Canadian Network on Hepatitis C (CanHepC) and the CIHR Canadian HIV and STBBIs Clinical Trials Research Network (CTN+).