Ethical Implications of Artificial Intelligence in Vaccine Equity: Protocol for Exploring Vaccine Distribution Planning and Scheduling in Pandemics in Low- and Middle-Income Countries
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic highlighted significant disparities in vaccine distribution, particularly in low- and middle-income countries (LMICs). Artificial intelligence (AI) has emerged as a potential tool to optimize vaccine distribution planning and scheduling. However, its ethical implications, including equity, transparency, bias, and accessibility, remain underexplored. Ensuring ethical AI implementation in vaccine distribution is crucial to addressing health equity challenges worldwide. OBJECTIVE: This study aims to assess the ethical implications of AI-assisted vaccine distribution planning and scheduling in LMICs during pandemics. It seeks to evaluate AI's role in ensuring equitable vaccine access, analyze ethical concerns associated with its deployment, and propose an ethical framework to guide AI-based vaccine distribution strategies. METHODS: Our multiphase qualitative research approach will combine a systematic scoping review, a witness seminar with key stakeholders (health care professionals, AI developers, policymakers, and bioethicists), and a meta-synthesis of findings. The scoping review will follow PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, focusing on studies from 2019 to 2023. The witness seminar will provide firsthand insights into AI's ethical impact on vaccine equity. Thematic content analysis and qualitative coding will be used for data interpretation, with findings integrated into a policy-driven ethical framework. RESULTS: This study received institutional ethical approval in October 2023. Recruitment commenced in mid-August 2024 through email requests to prospective participants, and recruitment for the witness seminar (focus group discussion) is still ongoing, with 7 expert participants confirmed. Data collection is projected to conclude by August 2025. Preliminary literature analysis from the scoping review is ongoing, and qualitative data analysis from the witness seminar is scheduled for September 2025. The final results and proposed ethical framework are expected to be published in early 2026. CONCLUSIONS: By examining the ethical implications of AI in vaccine distribution, this research will provide actionable recommendations for policymakers, health care organizations, and AI developers. The findings will contribute to the discourse on responsible AI deployment in health care worldwide, ensuring transparency, fairness, and inclusivity in pandemic response strategies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/76634.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.137 | 0.167 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.098 | 0.027 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".