Exploring Artificial Intelligence-Based Distribution Planning and Scheduling Systems’ Effectiveness in Ensuring Equitable Vaccine Distribution in Low-And Middle-Income Countries—witness Seminar Approach

Abstract

Background: During the COVID-19 pandemic, the global issues of vaccine access and equity, particularly in low-and middle-income countries (LMICs), came to the forefront. Simultaneously, there was notable advancement in artificial intelligence (AI) and its potential applications in vaccine distribution and scheduling. In response to these developments, we gathered insights, lessons, and perspectives to inform future strategies for AI-based distribution planning and scheduling systems’ effectiveness in ensuring equitable vaccine distribution in LMICs. Method: We conducted a scoping review, followed by two separate witness seminars held at different time points. Participants’ statements were transcribed, coded, categorized, and analysed, with the findings organized thematically. These findings subsequently informed the development of the ethical framework. Results: A total of 28 articles were included in the scoping review. For the witness seminar, there were eight witness participants, three moderators, and two observers, engaging in discussions that lasted an average of one hour and 40 min for both seminars. In the transcript of the first witness seminar, 192 codes, 22 categories, and five themes were identified through inductive coding. In contrast, the second seminar’s transcript yielded 159 codes, 11 categories, and five themes through open coding. The coding and analysis processes were conducted independently and then collectively validated to minimize bias in judgment and interpretation. Discussion: Despite AI’s potential, several challenges can impede the effective deployment of AI in vaccine distribution, especially in low-resource settings. These challenges include ensuring equitable access and managing distribution priorities, as well as addressing data management issues and technological limitations. Additionally, leveraging data and technology to optimize the distribution process is crucial, alongside evaluating the effectiveness and governance of AI systems. Ultimately, ensuring equity and inclusivity in AI-driven vaccine distribution remains paramount for maximizing its impact. Conclusion: This study highlights the effectiveness of AI implementation in vaccine distribution and equity, especially during the pandemic in low- and middle-income countries (LMICs), where achieving vaccine equity remains a significant challenge. It proposes an ethical framework consisting of 10 core components along with 11 implications and policy recommendations aimed at promoting the responsible and equitable use of AI support systems to enhance vaccine equity in future pandemics. © The Author(s) 2025.