From algorithms to negotiations: Why health diplomacy must adapt
Bibliographic record
Abstract
Health diplomacy traditionally relies on consensus-building across nations, yet the accelerating integration of artificial intelligence (AI) into health systems poses new governance challenges. Rapidly changing geopolitical conditions-exemplified by shifts in U.S. global health funding and the expansion of AI beyond national boundaries-underscore the urgency of rethinking traditional approaches. This paper, based on insights from the Prince Mahidol Award Conference 2025 side meeting on "Navigating the Future: AI & Global Health Diplomacy," examines how AI can reshape the practice of health diplomacy, both empowering and unsettling global health objectives. We first explore the promise of AI in enhancing disease surveillance, resource allocation, and progress toward universal health coverage. However, inadequate governance can exacerbate inequalities, particularly if AI remains in the hands of profit-focused enterprises or if digital divides persist. Health diplomacy, therefore, must expand its purview to include technical literacy, data ethics, and robust regulatory frameworks that safeguard equity and transparency in AI design and deployment. To illustrate these dynamics, we emphasise the interplay of social, political, commercial, and digital determinants of health, each feeding into AI-driven innovations. Strong diplomatic engagement is critical to ensuring that AI becomes a tool for mutual benefit rather than a catalyst for further fragmentation. Effective policies must integrate environmental sustainability considerations alongside cross-sector collaboration. We conclude that, although AI cannot replace the vital human element of negotiation and trust-building, it can substantially enhance global health outcomes when governed ethically and inclusively. The future of health diplomacy, shaped by AI, requires agile adaptation and unified strategies to preserve equity and planetary well-being.
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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 | 0.053 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".