Can AI Diplomacy Build a Fairer Future for Global Health?

On World Science Day for Peace and Development, observed on 10 November, the focus on scientific diplomacy offers an important opportunity to examine how countries can cooperate to ensure that AI advances medicine without deepening global inequalities.

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Why Does International Scientific Cooperation and Ethical AI Governance Matter for the Future of Medicine?

Ethical AI in healthcare is an international challenge that no single government, research institution, or technology company can address alone.

As artificial intelligence moves to span clinical decision-making, medical research, drug development, and public health, questions about patient rights, data governance, algorithmic bias, and equitable access increasingly cross national borders.

Science Diplomacy and Healthcare Enter the AI Debate

The United Nations Educational, Scientific and Cultural Organization (UNESCO) 2026 theme for World Science Day for Peace and Development is “Common ground through science diplomacy.”

The annual observance highlights the role of science in society and the importance of international solidarity and cooperation around shared challenges.

The theme is particularly relevant to artificial intelligence in healthcare.

Medical research, disease surveillance, pharmaceutical innovation, and healthcare delivery increasingly depend on technologies and datasets that extend beyond national borders. Yet the policies governing these systems remain fragmented.

An AI model developed using clinical data from one country may eventually be deployed in hospitals across several continents; or, a drug discovery platform trained on particular population datasets may influence which therapeutic candidates receive investment, and a diagnostic algorithm developed in a well-resourced healthcare system may perform differently when introduced into a region with different disease prevalence, demographics, clinical infrastructure, or diagnostic practices.

These are not exclusively technological problems. They involve public policy, scientific standards, human rights, national sovereignty, and international trust.

Scientific diplomacy provides a framework for addressing these challenges through dialogue and cooperation.

Such diplomacy would bring scientists, governments, research institutions, international organisations, and industry together to develop shared approaches to problems that cannot be solved independently.

For AI in healthcare, that cooperation could influence how medical algorithms are evaluated, how research data is shared, how risks are monitored, and how the benefits of innovation are distributed.

Why Does Ethical AI Need More than National Regulation?

Governments are increasingly recognising that responsible artificial intelligence requires oversight.

However, national regulations alone cannot resolve every issue associated with technologies that are developed, trained, distributed, and deployed internationally.

UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted in November 2021, established the first global standard of its kind. Applicable to all 194 UNESCO Member States, the framework places human rights and dignity at the centre of AI governance.

Its principles include proportionality, safety, privacy, transparency, fairness, accountability, and human oversight.

Importantly, the global recommendations also address international cooperation and the need to ensure that AI development benefits countries with different levels of technological and economic capacity.

The recommendation is particularly relevant to healthcare because it recognises health and social wellbeing as a dedicated area of policy action. It calls for AI applications in healthcare to be safe, effective, scientifically and medically supported, and consistent with human rights obligations.

This is critically important for an AI system that could well meet its technical performance targets while still creating unacceptable outcomes.

An algorithm might perform well across its development dataset but produce less reliable results for underrepresented populations; or a clinical decision-support tool might improve efficiency while making it difficult for healthcare professionals to understand or challenge its recommendations.

A generative AI system might produce convincing medical information that contains errors, creating risks for patients and clinicians.

Ethical AI governance must therefore examine not only whether an AI system works, but also whom it works for, who bears responsibility when it fails, and whether those affected have meaningful protection and recourse.

International cooperation can help establish common expectations for these questions.

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Why Can’t AI in Healthcare Be Separated from Data Inequality?

One of the most significant challenges facing the wide use of AI in healthcare is the uneven distribution of data and technological resources associated with the technology.

Advanced AI systems require access to substantial quantities of high-quality data, computing infrastructure, specialist expertise, and research funding.

These resources are not distributed equally.

Many AI models are developed using datasets collected in countries with established digital health infrastructure and substantial research investment.

However, populations in lower- and middle-income countries may have different genetic characteristics, disease burdens, environmental exposures, healthcare access patterns, and treatment histories.

This creates a risk that AI systems will reproduce existing gaps in medical research.

For example, a diagnostic model trained predominantly on images or clinical records from one population may not perform equally well in another.

Similarly, predictive models built around healthcare systems with extensive electronic records may be difficult to transfer into settings where medical information remains fragmented or paper-based.

The consequences are wide-ranging. Bias in drug discovery, clinical trial recruitment, disease surveillance, and treatment recommendations could influence which populations benefit from pharmaceutical innovation.

Addressing these problems requires more than asking technology companies to diversify their datasets.

It requires investment in local research capacity, data infrastructure, scientific education, and meaningful participation in international research partnerships.

Research institutions and healthcare organisations in the Global South should be equal participants in designing and evaluating these systems, rather than simply becoming end users of technologies developed elsewhere.

This is where scientific diplomacy is vital.

International partnerships can support the development of research networks, shared infrastructure, training programmes, and governance frameworks that enable countries to contribute knowledge while protecting their populations and national interests.

The objective should be to ensure that differences in resources do not automatically translate into differences in access to safe and effective innovation.

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Can International Standards Make AI Safer for Patients?

The World Health Organization (WHO) has also recognised the need for coordinated oversight for AI in healthcare.

Its guidance on the ethics and governance of artificial intelligence for health, including large multimodal models, examines the opportunities and risks associated with generative AI in medical and public health settings.

These systems can process different forms of information, including text and images, and could support clinical research, health education, diagnosis, and drug development.

However, their capabilities also introduce risks involving inaccurate outputs, automation bias, privacy, cybersecurity, and the potential misuse of sensitive health information.

The WHO guidance places responsibility on governments, developers, and healthcare providers to establish appropriate safeguards.

For healthcare, organisations need to understand how systems are developed, what data they use, how performance is assessed, and how health outcomes are monitored after deployment.

International scientific cooperation could strengthen this process by supporting shared research methodologies, independent evaluations, interoperable standards, and the exchange of evidence about system performance.

It could also help address the problem of inconsistent regulatory expectations.

Pharmaceutical companies and healthcare technology developers operating across multiple markets must navigate different requirements for data protection, medical device regulation, clinical evidence, and AI oversight.

Greater international alignment could reduce unnecessary duplication while preserving the ability of individual countries to protect patients and enforce their own laws.

However, harmonisation should not mean lowering standards to achieve commercial convenience.

Shared frameworks must provide meaningful protection, particularly for populations with limited regulatory resources.

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Scientific Diplomacy Must Include Those Affected by It

There is a fundamental difference between international cooperation and simply exporting technological solutions.

For scientific diplomacy to have a meaningful impact on AI in healthcare, countries must have a voice in the development of the systems, standards, and policies that will affect their populations.

This is especially important for emerging economies and those in the Global South.

AI development is concentrated among organisations with access to advanced computing infrastructure, specialist talent, investment, and large datasets.

Without deliberate efforts to broaden participation, the countries with the greatest healthcare needs may have the least influence over how medical AI develops.

International partnerships can help change this dynamic.

Universities, pharmaceutical companies, public health agencies, technology developers, and research networks can collaborate on locally relevant datasets, clinical validation, disease surveillance, and AI applications designed for resource-constrained healthcare environments.

Such partnerships must also address data ownership, consent, privacy, benefit-sharing, and intellectual property.

Scientific collaboration should not become a mechanism through which valuable health information is extracted from populations without appropriate safeguards or reciprocal benefits.

For Africa, the opportunity is significant. The continent faces substantial challenges involving healthcare access, disease surveillance, diagnostic capacity, and specialist resources.

Carefully governed AI could support earlier disease detection, improve research efficiency, strengthen clinical services, and help extend the reach of limited healthcare infrastructure.

But these benefits will depend on reliable infrastructure, local expertise, representative data, effective regulation, and genuine participation in decision-making.

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The Pharmaceutical Industry Has the Key Role in Building Trust

Pharmaceutical companies are increasingly using AI across drug discovery, clinical development, pharmacovigilance, manufacturing, and regulatory operations.

These applications create new opportunities for scientific collaboration, but they also introduce questions about the reliability, reproducibility, and governance of AI-supported decisions.

A drug discovery model trained on incomplete or unrepresentative biological data may influence which candidates progress; or an AI-supported clinical trial platform may affect patient selection or recruitment, while an automated safety system may influence how adverse events are identified, prioritised, and reported.

In each case, the quality of the underlying data, the suitability of the model, and the ability to explain and audit its outputs matter.

International cooperation could support more consistent expectations for evaluating these systems, sharing relevant safety evidence, and maintaining accountability across research and development partnerships.

The pharmaceutical industry should also participate in conversations about equitable access.

If AI accelerates drug development but the resulting treatments remain inaccessible to populations that were excluded from the research process, the broader promise of innovation remains incomplete.

Responsible AI requires organisations to consider the entire innovation lifecycle, from data collection and model development to clinical application, regulatory assessment, and patient access.

A Shared Scientific Future Requires Shared Responsibility

The World Science Day for Peace and Development is a reminder that scientific progress depends on the institutions, relationships, and international frameworks that enable knowledge to be shared and applied responsibly.

Artificial intelligence could help address some of healthcare’s most difficult challenges.

It could accelerate research, strengthen disease surveillance, improve diagnostic support, and expand access to scientific expertise. However, these possibilities will not automatically translate into better health outcomes.

Without international cooperation, AI governance risks becoming fragmented.

Without representative data, medical systems may reproduce existing inequalities.

Without transparency and accountability, public trust may weaken.

And without meaningful participation from emerging economies, the benefits of AI-driven healthcare could remain concentrated among a relatively small group of countries and organisations.

Scientific diplomacy offers a way to bring these concerns into a shared international conversation.

The question is how the industry can ensure that innovation is scientifically credible, ethically governed, internationally inclusive, and ultimately beneficial to patients.

At Pharmatica, we believe the future of life sciences depends not only on what science can achieve, but on how responsibly its advances are developed, governed, and shared.

AI may transform medicine, but international cooperation, scientific integrity, and human accountability will determine whether that transformation benefits everyone.

Pharmatica: Insight. Connection. Impact.