Introduction
What Does “Governance” Mean in Global Health Innovation?
The Historical Role of the WHO
The Rise of Private AI Drug Discovery Ecosystems
Diverging Centers of Authority
Equity and Access: Who Benefits?
References and Further Reading
Global health governance is increasingly shaped by the interaction between multilateral institutions and rapidly expanding private technological ecosystems. The withdrawal of the United States from the WHO alongside the emergence of corporate AI drug-discovery ecosystems raises fundamental questions about who now shapes research priorities, ethical standards, and equitable access to future medical breakthroughs.
Image credit: raker/Shutterstock.com
Introduction
The withdrawal of the United States from the World Health Organization (WHO) has been described as a significant disruption to global health governance, raising concerns about weakened coordination in research prioritization and pandemic preparedness. The United States has historically been one of the WHO’s largest financial contributors and an influential actor in shaping global health initiatives, raising questions about the stability of multilateral health financing and leadership in its withdrawal.1,3,5
At the same time, the partnership between NVIDIA and Eli Lilly to establish an artificial intelligence (AI)-driven drug discovery lab signals a shift toward technology-centered biomedical leadership. Together, these developments suggest a bifurcation between multilateral norm-setting and high-velocity innovation ecosystems, prompting renewed questions about who ultimately governs the direction of global biomedical progress.1,4,5
This article explores how the United States’ withdrawal from the WHO and the rise of AI-driven partnerships, such as the NVIDIA-Eli Lilly collaboration, are reshaping the governance, equity, and future direction of global health innovation.
Want to revisit this later? Grab the free downloadable version here.
What Does “Governance” Mean in Global Health Innovation?
Governance in global health innovation does not imply centralized command or a single decision-making authority. Instead, it reflects the systems, norms, incentives, and institutional arrangements that shape how biomedical progress occurs. It determines which diseases receive research attention, how financial and scientific resources are distributed across regions and populations, and what ethical and safety standards guide experimentation. Governance also determines how intellectual property is created and assigned, who has access to new biomedical products, and at what cost. Therefore, governance should be understood as a set of processes involving priority-setting, accountability, and resource allocation rather than as strictly regulatory functions.1,3,4
Importantly, governance extends beyond formal authority. It is typically accomplished through treaties, regulatory frameworks, and intergovernmental agreements; however, there are other ways of achieving governance through mechanisms that operate in the background, such as venture capital, procurement mechanisms, and data ownership structures. In the context of artificial intelligence for health, governance also includes ethical oversight of data collection and algorithm design, transparency, and accountability mechanisms to ensure that technologies respect human autonomy and public health priorities.1 For example, there are multiple sources of market forces (balanced and fair) that can influence the direction of research.
Increasingly, governance is embedded within technological systems themselves. Algorithmic design, training datasets, computational infrastructure, and platform access rules influence which biological questions are explored and which innovations are technically feasible. As AI becomes central to drug discovery, governance is no longer confined to policy documents. It is encoded in software architecture and data ecosystems that silently structure the boundaries of scientific possibility.1,3,4
The critical challenge is no longer whether private actors will hold power in global health, but whether multilateral and civil society institutions can develop the technical fluency and political will to ensure accountability beyond traditional frameworks - so that who benefits from AI-driven breakthroughs is shaped by public need, not just private investment.
Dr.Yanzhong Huang, Council on Foreign Relations, Associate Professor, Seton Hall University
The Historical Role of the WHO
Historically, the WHO has been the key player in setting global health research priorities, although it does not directly finance most biomedical innovation. Through its priority disease lists, which include neglected tropical diseases and emerging pathogens, the organization has brought the world's focus to conditions that would otherwise receive minimal investment. Its pandemic preparedness frameworks and coordination systems during global emergencies have aligned research efforts across countries, especially during a time of crisis.
WHO has also provided normative guidance on vaccine allocation, antimicrobial resistance, and outbreak response, and has affected the definition of urgency and the mobilization of resources. This has influenced funding priorities, research partnerships, and policy discussions by framing some health threats as global concerns.1,3
Health for All - Universal Health Coverage, a promise we must deliver!
Video credit: who/Youtube.com
WHO is not directly empowered over the member states despite this influence. It has been granted considerable power in the form of normative legitimacy rather than coercion. The organization sets standards that countries voluntarily adopt, such as international health regulations, the experts' advisory committee, consensus-building, and the establishment process through a technical guideline. Its authority derives largely from convening power, scientific expertise, and the ability to coordinate 194 member states in addressing transnational health threats. WHO’s strength lies in moral authority and agenda control in this stratified system of global health governance as opposed to legal enforcement and financial control.1,3
The Rise of Private AI Drug Discovery Ecosystems
The emergence of private AI-powered drug discovery ecosystems represents a structural shift in biomedical innovation. High-performance computing, generative models, and large-scale data analytics are cornerstones of therapeutic development, as demonstrated by the partnership between NVIDIA and Eli Lilly.
The companies have announced plans to invest up to $1 billion in an AI co-innovation laboratory designed to accelerate drug discovery through large-scale computational infrastructure and machine learning systems. In this model, AI-powered computational platforms accelerate molecular screening and candidate identification, shifting the locus of innovation from multilateral coordination toward proprietary data ecosystems and advanced infrastructure. Cutting-edge discovery increasingly unfolds within technology-driven corporate environments rather than intergovernmental research frameworks.4,5
Biomedical Intelligence at Scale with Lilly's AI Factory
Video credit: NVIDIA/Youtube.com
Within AI-led pipelines, priority-setting mechanisms differ from traditional public health models. Research targets may be selected according to commercial viability, dataset availability, and investor expectations. Diseases supported by extensive proprietary molecular and clinical datasets are more readily modeled, while neglected conditions with limited digital representation risk marginalization. Data scarcity can translate directly into innovation scarcity, creating a subtle governance shift in which the absence of data shapes scientific attention.
Transparency and accountability also emerge as key concerns: questions remain about dataset disclosure, bias auditing, ethical standards for synthetic molecule design, and intellectual property allocation for AI-generated compounds. International organizations have warned that insufficient oversight of AI systems could lead to algorithmic bias, unequal access to technology, and reduced accountability in medical decision-making. Although regulatory agencies evaluate the safety and efficacy of final products, few global standards govern upstream AI model training, leaving oversight fragmented and largely downstream rather than embedded in algorithmic design.1,4,5
Diverging Centers of Authority
Global health innovation is increasingly shaped by diverging centers of authority. Compared with the conventional cycle (hypothesis to preclinical), AI ecosystems can produce candidate molecules more quickly. Conversely, multilateral institutions prioritize consultation, agreement, and the fairness of access. Although technological acceleration offers speed, it may leave limited time for coordinated global access strategies. It is not always about supremacy but rather timeliness: Can rapid innovation remain aligned with shared global priorities? 1,3,4,5
At the same time, data sovereignty and infrastructure power are reshaping governance. Biomedical data are commonly stored in either national repositories or cloud storage systems, and nations are increasingly controlling genomic and health information. Infrastructure providers acquire structural power over who can have a meaningful involvement in the AI-driven discovery.1,3,4,5
Regulatory fragmentation adds further complexity, as divergent national approaches to AI oversight may produce inconsistent validation standards and uneven approval pathways, potentially widening disparities in access and safety. Although industry consortia can establish de facto technical norms and regulators often adapt to technological realities, market-driven standards do not automatically prioritize equity. Investment flows, shareholder expectations, and infrastructure ownership increasingly shape research agendas, suggesting a structural inflection point between multilateral coordination and technology-centered acceleration.1,3,4,5
Equity and Access: Who Benefits?
The central governance question is ultimately distributive: who benefits from accelerating biomedical innovation? As AI reshapes drug discovery, it may expand therapeutic breakthroughs, yet it also risks deepening disparities between data-rich and data-poor populations. If multilateral institutions like the WHO are unable to adapt quickly, technological ecosystems led by private sector firms like NVIDIA and Eli Lilly may increasingly set the agenda.
As a result, governance is diffusing from public institutions and regulators towards commercial corporations and capital markets. Some analysts warn that this shift may concentrate decision-making power among a small number of technology firms and private funders, potentially reshaping global health priorities in line with commercial interests rather than public health needs.3
The U.S. withdrawal from the WHO accelerates a fragmentation of multilateral governance in global health, creating space for private AI-driven biomedical innovation systems - often dominated by powerful tech-pharma partnerships - to exert greater influence over research priorities, data governance, and equitable access to therapies.
Dr.Yanzhong Huang, Council on Foreign Relations, Associate Professor, Seton Hall University
The challenge is not to decide between one model or another, but rather to integrate them before the acceleration of innovation becomes structural inequity.
References and Further Reading
- Aremu, S. O., Adamu, A. I., Obeta, O. K., Ibe, D. O., Mairiga, S. A., Otukoya, M. A., & Barkhadle, A. A. (2025). The United States withdrawal from the world health organization (WHO), its implications for global health governance. Globalization and Health. 21(1). DOI:10.1186/s12992-025-01137-0, https://link.springer.com/article/10.1186/s12992-025-01137-0
- BBC News. (2026, January 23). US officially leaves World Health Organization. https://www.bbc.co.uk/news/articles/cn9zznx8qdno
- Yazdi-Feyzabadi, V., Haghdoost, A. A., McKee, M., Takian, A., Bradley, E., Brugha, R., Eyal, N., Eybpoosh, S., Gostin, L., Ikegami, N., Kickbusch, I., Labonté, R., Mannion, R., Norheim, O. F., Shiffman, J., & Karamouzian, M. (2025). The United States withdrawal from the World Health Organization: Implications and challenges. International Journal of Health Policy and Management. 14. DOI:10.34172/ijhpm.9086, https://doi.org/10.34172/ijhpm.9086
- World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. https://www.who.int/publications/i/item/9789240029200
- HealthTech Magazine. (2026, January 26). NVIDIA and Lilly announce co-innovation AI lab to accelerate drug discovery. https://healthtechmagazine.net/article/2026/01/nvidia-and-lilly-announce-co-innovation-ai-lab-accelerate-drug-discovery
Last Updated: Mar 5, 2026