The United States is currently engaged in a rapid deployment of artificial intelligence (AI) across various critical sectors, including financial markets, defense strategies, and social media platforms. However, a significant oversight persists: AI’s profound capabilities remain largely underutilized in an area of paramount importance for American security—the proactive prevention of future pandemics. While substantial capital is directed towards AI tools designed to optimize economic activities and consumer behaviors, comparatively meager investments are allocated to global disease surveillance systems that possess the potential to halt outbreaks before they ever threaten US territory. This discernible imbalance is not merely a squandered opportunity; it represents a critical national security vulnerability that demands immediate attention from global healthcare stakeholders and policymakers.

Emerging infectious diseases, ranging from novel influenza strains to recurrent threats like Ebola and Zika, continue to surface globally. Governments frequently dedicate extensive resources to mitigating geopolitical and technological risks, yet often allocate disproportionately less to comprehensive pandemic preparedness. Neglecting robust disease surveillance initiatives abroad essentially constitutes a cost-shifting exercise, transferring the burden from preventative budgets to the far more substantial expenditures associated with emergency response and economic recovery in the aftermath of a crisis. As explicitly articulated in the US administration’s America First Global Health Strategy, global health security is deemed indispensable for making “America safer, stronger, and more prosperous.” This foundational principle, in our editorial opinion, should unequivocally guide current AI investment strategies.

Pandemics: A Clear and Present National Security Risk

Pandemics extend far beyond mere public health emergencies; they are profound systemic shocks capable of destabilizing national security and economic foundations. American enterprises rely heavily on a healthy workforce, resilient supply chains, and predictable market conditions—all of which are severely disrupted by the rapid spread of infectious diseases. The devastating impact of COVID-19, which tragically claimed over one million American lives, served as a stark and painful reminder of this reality. Without adequate preparation and a robust global healthcare infrastructure, no amount of individual wealth or corporate resilience can unilaterally contain biological threats to the broader American populace.

Existing surveillance methodologies leave both American citizens and international patients vulnerable due to substantial data deficiencies. A primary reason for AI’s underutilization in pandemic prevention is that vast swathes of the world remain largely invisible to advanced analytical systems. The majority of medical and epidemiological data employed to train sophisticated AI models originates from high-income countries. This creates a critical blind spot, given that many virulent outbreaks either emerge or accelerate in low- and middle-income countries (LMICs). While private healthcare providers in these regions do generate valuable clinical information, the absence of secure and integrated systems to feed this data into universal surveillance platforms means its immense public health value is frequently lost. This also impacts the quality of care that can be provided globally.

The consequence is a pervasive global data asymmetry: while advanced analytics can achieve high accuracy in certain well-resourced environments, significant surveillance gaps persist in others. Pathogens, by their very nature, disregard national borders. Therefore, these surveillance voids in one healthcare destination directly translate into delayed detection, slower response times, and an elevated risk for populations at home and for international patient care worldwide. From a cross-border healthcare perspective, this asymmetry is particularly concerning, as it undermines the very foundations of international health security.

Bridging Global Health Data Gaps with AI for Enhanced Patient Travel Safety

Crucially, the technological solutions required to bridge these critical data gaps are already available. Global AI spending was projected to reach an astounding $1.5 trillion by 2025, unequivocally demonstrating that a lack of capital for AI development is not the fundamental constraint. Within the United States, however, healthcare AI spending totaled only $1.4 billion last year, and even this figure can be misleading. Private and commercial healthcare AI investments are predominantly skewed towards improving internal system efficiencies, rather than bolstering population-level disease surveillance. Public health surveillance infrastructure, which fundamentally relies on robust clinical data foundations, remains comparatively underfunded and fragmented, particularly across international and inter-agency boundaries, such as those between national security and health organizations. This fragmentation poses a challenge for patient travel and overall wellness tourism, as trust in global health systems is paramount.

In our expert opinion, the core challenge is not a deficiency in technology or financial resources. Rather, it is the systemic failure to effectively deploy and integrate AI-enabled tools in regions where early outbreak detections can prevent widespread risk to American lives and enhance global quality of care.

AI-Enabled Disease Surveillance in Practice: A Blueprint for International Patient Care

Several nations are already providing compelling demonstrations of what AI-enabled surveillance can achieve, offering valuable insights for improving international patient care and making regions safer healthcare destinations. During the height of the COVID-19 pandemic, Rwanda leveraged AI to safeguard frontline health workers and significantly enhance disease surveillance capabilities. This included deploying robots for vital sign monitoring and delivering food and medicines, alongside a sophisticated chatbot that provided guidance to 580,000 citizens between December 2020 and July 2022. Furthermore, Rwanda addressed its 2024 Marburg virus outbreak with predictive analytics powered by machine learning, AI-driven diagnostics, and robots for decontaminating medical units. Rwanda’s proactive and effective integration of AI to contain deadly outbreaks exemplifies the immense promise of a truly comprehensive global surveillance system, bolstering confidence for any international patients considering patient travel to the region.

Regional initiatives likewise showcase considerable potential. The Africa Centers for Disease Control and Prevention (Africa CDC) oversees the Integrated Disease Surveillance and Response (IDSR) system across its 55 member states. This system has seen its real-time data sharing protocols significantly strengthened since the onset of COVID-19. Initial support for Africa CDC and IDSR was provided by the United States, illustrating how strategic, targeted investments can catalyze the development of large-scale, impactful disease detection systems. We believe that a more comprehensive surveillance architecture, one that fully harnesses AI and seamlessly integrates public institutions, private-sector capabilities, and trusted local partners, will substantially fortify early warning and response mechanisms, making global healthcare more resilient.

Scaling AI Surveillance Through Global Health Infrastructure and Governance

This strategic approach is not a mere theoretical concept. Established institutions, such as the Global Fund to Fight AIDS, Tuberculosis and Malaria, have already demonstrated remarkable achievements by supporting national health information systems like DHIS2 and developing interoperable reporting platforms, including its Aggregate Data Exchange (ADEx). Through the integration of data from both public and private providers, these systems have demonstrably strengthened cross-border outbreak detection and response capacities in LMICs. However, these vital models remain critically underfunded and unevenly deployed, thereby exposing, rather than insulating, the US public from the persistent threat of pandemic risk. For countries aspiring to become leading healthcare destinations, investing in such infrastructure is crucial.

It is our firm conviction that the United States must also make substantial investments in the foundational digital infrastructure that enables effective AI deployment in LMICs. This includes ensuring reliable internet connectivity, robust cloud computing capacity, and the development of interoperable national health data systems. These investments should not be viewed as charitable endeavors; they are strategic force multipliers that effectively reduce critical blind spots and significantly strengthen global detection networks, ultimately protecting US citizens and enhancing the safety of patient travel globally.

Finally, AI development must be meticulously aligned with the practical realities and urgent needs of global public health. The datasets utilized to train AI models must be highly varied, truly representative of diverse populations, and transparent in their origin and application. Moreover, institutions such as the Global Fund, which possess extensive experience in delivering innovation at scale and coordinating efforts across the private sector and community stakeholders, should be granted a prominent seat at the table in all critical AI governance discussions. Their expertise in global healthcare delivery is invaluable.

Building Robust Global Health Data Systems: Infrastructure, Governance, and AI Integration

Artificial intelligence is frequently framed within the context of a race for economic or military supremacy. However, one of its most profoundly consequential tests may well be its capacity to help avert the next global health catastrophe before it even begins. Pandemics, capable of overwhelming even the most powerful nations, are an inevitable aspect of our interconnected world. Yet, crucially, they are also often predictable—and, with the right tools, preventable. This understanding is reflected in the actions of nations like China and Russia, which are notably increasing their health surveillance expenditures and access to data in Africa and Asia, recognizing the strategic importance of global healthcare data.

Investing in comprehensive global disease surveillance is not an act of mere generosity; it is, fundamentally, an act of self-defense. It directly translates into American lives saved and a more secure environment for cross-border healthcare and international patients. The economic toll exacted by the COVID-19 pandemic on the US economy was immense. A 2024 report from the nonpartisan Heritage Foundation commission revealed an estimated minimum of $18 trillion in economic costs to the US attributable to the COVID-19 pandemic. US health spending specifically to counter COVID-19 reached a staggering $4.6 trillion. Allocating even a small fraction of that colossal sum to harness AI for proactive disease outbreak surveillance represents an essential insurance policy—one that citizens, consumers, and workers simply cannot afford to neglect, especially as we consider the future of medical tourism and wellness tourism.

Bottom Line: Securing the Future of Global Health

  • Strategic Imperative: The underutilization of AI in global disease surveillance represents a critical national security vulnerability, despite its proven potential in other sectors. Investing in this area is a strategic necessity, not merely a health initiative.
  • Data Asymmetry: Significant data gaps persist, particularly in LMICs, hindering effective AI-driven surveillance. Bridging these gaps is crucial for global health security and effective international patient care.
  • Proven Models: Countries like Rwanda and regional bodies like Africa CDC demonstrate the tangible benefits of AI in disease detection and response, offering a blueprint for other healthcare destinations.
  • Infrastructure Investment: The United States must invest in fundamental digital infrastructure (connectivity, cloud, interoperable data systems) in LMICs to enable effective AI deployment, strengthening cross-border healthcare.
  • Governance and Collaboration: AI development and governance must align with global public health needs, involving experienced institutions like the Global Fund to ensure representative data and scalable innovation. This will enhance the overall quality of care globally.
  • Economic Prudence: Proactive investment in AI-driven surveillance is a cost-effective insurance policy against future pandemics, far less expensive than emergency response and economic recovery, safeguarding patient travel and wellness tourism.

The news signal for this article was referred from: https://nationalinterest.org/blog/techland/melding-global-health-and-ai-for-national-security