Artificial intelligence is slowly becoming part of the global health system. It is helping researchers work with huge amounts of health data, supporting disease surveillance, assisting with medical research, improving health information systems, and creating new ways to communicate with patients and communities.
For NGOs, this development is particularly interesting.
Many nonprofit organizations work in places where health systems are under pressure, health workers are limited, information is difficult to collect, and communities may be spread across remote or hard-to-reach areas. In these settings, even a small improvement in how health information is collected, analyzed, or shared can make a meaningful difference.
AI could help organizations identify health risks earlier, support frontline workers, improve the delivery of services, and reach people with information in more accessible ways.
But global health is not a field where speed should come at any cost.
A wrong recommendation in a marketing campaign may be inconvenient. A wrong recommendation in a health program can affect a person’s diagnosis, treatment, privacy, or safety.
That is why the future of AI in global health cannot be discussed only in terms of innovation.
It also has to be a conversation about trust, ethics, human oversight, fairness, and who gets to make the final decision.
The World Health Organization has been addressing these concerns for several years. Its guidance on the ethics and governance of AI for health says that AI has significant potential to improve diagnosis, treatment, research, drug development, public-health surveillance, and outbreak response, but that ethics and human rights need to be at the center of its design, development, and use.
That balance between opportunity and responsibility is likely to become one of the biggest issues facing NGOs and global health organizations in the coming years.
Why global health is turning to AI
Healthcare generates enormous amounts of information.
Patient records, laboratory results, vaccination information, disease surveillance reports, medical images, public health statistics, supply chain data, and research studies. Going through all of this material can be complicated and time-consuming for a human team.
AI is good at handling a massive amount of data.
It is able to recognize trends, sort, and assist users in establishing connections that might otherwise take a long time to uncover. This is useful in clinical settings as well as public health.
Among other things, the WHO has outlined possible use in diagnosis and treatment, health research and pharmaceutical development, and public health functions such as surveillance and outbreak investigation.
For NGOs, the possibilities extend beyond hospitals.
A community-health project could use technology to manage health reports from multiple sites. A disease-control program could use data to pick up signals of unusual activity. A maternal health program could use digital services to inform and support frontline health workers. A humanitarian organization could link health data with data on displacement, climate, and/or food insecurity to reveal new threats to health.
It is not technology for technology’s sake.
The objective is better health.
AI could help NGOs reach people who are difficult to reach.
One of the biggest opportunities for NGOs is improving access.
Many nonprofit health programs work in rural communities, refugee settlements, conflict-affected areas, and places where healthcare workers are in short supply. Digital tools can sometimes help extend the reach of existing health services.
For example, AI-supported systems can help organize information for frontline health workers, translate health content into different languages, or provide basic information to people through digital channels.
This could be especially useful in multilingual settings.
A health organization may need to communicate information about vaccinations, maternal health, nutrition, or disease prevention to communities speaking several different languages. Technology can speed up the process of adapting information, although the final content still needs to be checked by people who understand the language and cultural context.
The opportunity is therefore not just about replacing work.
It is about helping limited teams reach more people.
This is important because the global health workforce remains under pressure. WHO estimates a projected shortfall of 11 million health workers by 2030, concentrated mainly in low- and lower-middle-income countries.
AI cannot solve that shortage by itself.
But it could potentially help health workers spend less time on certain administrative tasks and more time on patient care and community engagement.
AI can support disease surveillance and outbreak response.
Public health surveillance is another area where AI can be useful.
When a disease begins spreading, health authorities and NGOs need to understand what is happening quickly. They may need to examine reports from clinics, laboratories, communities, and other sources.
AI can help process information at a scale that would be difficult for people to handle manually.
WHO has specifically identified public health surveillance and outbreak response among the areas where AI could provide value.
This could become even more important as health threats become increasingly connected to climate change, urbanization, migration, and environmental changes.
An outbreak does not exist in isolation.
People may be moving between communities. Extreme weather can affect healthcare access. Water shortages can increase health risks. Conflict can disrupt vaccination and disease surveillance systems.
A health NGO working on the ground may therefore need to look at much more than medical data alone.
AI may help connect some of these information sources.
But the system still needs people who understand the context behind the numbers.
Data can help, but data can also mislead.
This is where the risks become more complicated.
AI systems learn from data.
If the data is incomplete, biased, or poorly collected, the results can also be problematic.
Global health has an enormous diversity of populations, languages, cultures, healthcare systems, and living conditions. A model that performs well in one country may not work equally well somewhere else.
A dataset may contain more information about urban hospitals than rural clinics.
It may include one population group more heavily than another.
It may be based on medical records that were collected differently across countries.
It may not adequately represent people who have limited access to health services in the first place.
That creates a serious concern for NGOs.
The people who are most underserved by healthcare systems may also be the people who are least represented in the data used to build health technologies.
If organizations are not careful, AI could make these inequalities harder to see rather than easier to address.
Global health cannot be one-size-fits-all.
This is one of the strongest reasons human oversight matters.
Imagine an AI health system developed using data from large hospitals in high-income countries. The system might perform well in those environments.
Now place it in a rural health center where equipment is limited, patient records are incomplete, and healthcare workers face very different conditions.
The system may still be helpful.
But it may also make assumptions that do not fit the local environment.
A technology designed without local context can unintentionally become another barrier.
This is why global health organizations need to think about where the data came from, who was represented, how the tool was tested, and who will actually use it.
Local health workers should not simply be expected to accept a system developed somewhere else.
They should have an opportunity to test it, question it, and provide feedback.
The same applies to communities.
Technology should be adapted to communities rather than expecting communities to adapt themselves to technology.
Human oversight is especially important in healthcare.
There is a major difference between using AI to summarize a document and using AI to influence a healthcare decision.
The higher the stakes, the stronger the need for human judgment.
A health worker may use an AI-supported system to organize information or identify possible warning signs. That does not mean the system should independently determine what happens next.
WHO’s guidance is clear that AI in health should be governed in ways that protect human autonomy, promote safety, and ensure accountability. The organization argues that stakeholders, including healthcare workers and the communities affected by AI, need to remain part of the process.
Human oversight is therefore not a barrier to AI.
It is part of making AI useful.
A healthcare professional may notice something that is not visible in the dataset.
A community health worker may understand a patient’s circumstances better than a model.
A doctor may recognize that a recommendation does not fit the patient’s situation.
A local NGO may know that an intervention will not work because of cultural or practical realities.
Those forms of knowledge matter.
The danger of treating AI like an expert
AI systems can sound confident.
That creates a particular problem in healthcare.
When a system produces an answer in a clear and professional tone, people can assume that it must be reliable.
But fluency is not the same as accuracy.
An AI system may generate incorrect information, misunderstand a case, or miss an important detail.
The problem becomes more serious when users stop questioning the system.
Healthcare workers need to remain able to disagree with AI recommendations.
Organizations need to create environments where questioning a technology is acceptable.
And AI systems used in health programs need monitoring even after they have been deployed.
WHO’s guidance on large multi-modal models, published in 2025, reflects the growing attention to newer forms of generative AI and the need to manage their risks in health settings.
As these models become capable of handling text, images and other forms of information, their potential applications in health may grow.
So will the governance challenge.
Patient privacy becomes even more important
Health data is among the most sensitive information an organization can hold.
People may share details about medical conditions, pregnancies, mental health, disabilities, medications, infectious diseases or family histories.
In humanitarian settings, health information may be connected with displacement status, identity and protection concerns.
The use of AI adds another layer to this challenge.
Organizations need to understand exactly what information is being collected, where it goes and who can access it.
They also need to think carefully about whether every piece of information being collected is actually necessary.
The fact that technology can process more data does not mean an organization should collect more data.
The WHO’s ethics and governance guidance emphasizes protecting privacy and confidentiality alongside other ethical principles.
For NGOs, that means data-protection practices should develop alongside AI adoption rather than being added later.
The rise of generative AI creates new questions
That’s right – generative AI is changing how you access your health information. Think about it: if you type in a health question and get an answer back almost instantaneously, seems pretty handy, huh? Not so fast – because it can be downright dangerous if the information is flawed.
Your health info isn’t just another Internet search. If you come across an inaccurate description of a historical event, you might simply learn a fact that’s wrong. But what if you came across health misinformation? You could make a choice that affects your health.
Therefore, NGOs working in health communication should be cautious of generative AI. While it is possible to use AI to draft up training sessions or translate them for different languages and set content for different audiences, a lot of information is really in need of human oversight. Isn’t it the public’s or world’s organizations’ responsibility, not the model?
AI could make global health more personalized
Can AI reinvent health care for the better? Let’s explore some of the ways it can help health organizations do away with the one-size-fits-all approach. For the longest time, health systems have had lots of data about you and your community. Now with analytics, it might be really easy to identify various needs and tailor services accordingly.
And for NGOs, this could mean tailoring health education, designing improved outreach efforts and optimizing scarce resources. Awesome, isn’t it? But all this optimism comes with some concerns.
The longer a system is, the more detail it could ask of you. But that’s where the main privacy concern comes in. Does need more data?
Is the value there?
When it comes to responsible AI management, more isn’t necessarily better. Instead, we should only gather what’s necessary for health-related reasons, and secure that data.
What happens when AI is wrong?
This may be the most important question of all.
Suppose an AI-supported system misses an important warning sign.
Suppose it produces a misleading health message.
Suppose a patient is incorrectly categorized.
Suppose a dataset contains a bias that causes certain communities to receive less attention.
- Who is responsible?
- The software provider?
- The health organization?
- The health worker?
- The donor?
- The government?
These situations demonstrate why accountability must be decided before the technology is introduced.
Organizations cannot simply blame the algorithm when something goes wrong.
The AI did not choose to enter a community.
People chose to build it, purchase it, deploy it and rely on it.
That means responsibility remains human.
Human oversight has to be meaningful
Having a person somewhere in the process is not enough.
Human oversight should mean that people actually have the ability to intervene.
A health worker should be able to reject an AI recommendation when it does not fit the patient’s circumstances.
An NGO should be able to pause a system when it detects harmful patterns.
A programme manager should be able to request an independent review.
A community should have a way to raise concerns when technology is affecting access to services.
This is what makes oversight meaningful.
It gives technology limits.
And in healthcare, limits can be a good thing.
Local NGOs have an important role to play
Much of the global AI conversation is dominated by technology companies, governments and major institutions.
NGOs and community organizations need a stronger voice in that conversation.
Local NGOs understand the realities of the populations they serve.
They know which services are difficult to access.
They know where data is missing.
They understand local languages and social norms.
They can often identify problems with a technology intervention much earlier than an organization working from a distance.
That makes them valuable not only as implementers of digital health projects but as partners in designing them.
A global health technology system should ideally be developed with input from the people who will actually use it.
That includes doctors and nurses, but it also includes community health workers, local NGOs, patients and caregivers.
The Global South should not simply be a consumer of health AI
This issue becomes even more important in low- and middle-income countries.
There is enormous potential for AI to support healthcare where resources are limited.
But there is also a risk that AI systems are created elsewhere and then exported into countries with very different healthcare environments.
A responsible global health approach should therefore ask more than whether technology can be deployed.
It should ask whether local institutions have enough control, skills and infrastructure to use it responsibly.
- Can local health workers understand the system?
- Can local organizations audit or question it?
- Can the system function with local data?
- Does it support local languages?
- Can local communities participate in decisions about its use?
- What happens if the technology provider changes its product or pricing?
- These questions are particularly important because dependency on external technology can become another form of dependency in the health system.
AI and the healthcare workforce
There is also a common fear that AI will eventually replace healthcare workers.
That is not the most useful way to look at the issue.
The global health workforce is already under enormous pressure, and the WHO projects a major shortfall by 2030.
The more immediate opportunity is to use AI to support those workers.
If technology can reduce administrative work, help organize information or make certain tasks easier, health workers may have more time for patients and communities.
But this only works when the technology is designed around the needs of health workers.
A poorly designed AI system can actually create more work.
Staff may spend time correcting automated mistakes, entering information in unnecessarily complicated formats or dealing with systems that do not fit their workflows.
AI should therefore be designed to support health workers, not simply to automate parts of their jobs.
NGOs need to think about the cost of AI too
AI adoption is often portrayed as a guarantee for cost savings.
This is not always true.
Organizations might incur expenses for software, computing resources, data storage, cybersecurity, technical skills, training, and upkeep.
There can also be unforeseen expenses.
Employees require time to familiarize themselves with new systems.
Data could require cleaning.
Current databases might need reconfiguration.
Policies may require revision.
Systems often need to undergo extensive testing.
For smaller NGOs, these expenses can be substantial.
Thus, funding for AI in global health should not solely aim at purchasing technology.
It should also back the personnel and infrastructure necessary for responsible use.
Donors have a role in responsible health AI
Funders are becoming increasingly interested in digital health and AI.
That can open new opportunities for NGOs.
But donors also influence how technology is adopted.
When funding is tied too closely to the launch of a new tool, organizations may feel pressure to demonstrate innovation quickly.
Responsible innovation takes longer.
- It requires testing.
- It requires community feedback.
- It requires training.
- It requires monitoring.
It requires the ability to stop or change a system when something is not working.
Funding models should account for these realities.
The goal should not be to count how many AI tools an NGO has adopted.
The goal should be to understand whether those tools are improving health outcomes.
AI should work with the wider idea of One Health
Discovered the worldwide health ‘virus’? Well-known worldwide health ‘virus’? Nicely, the World Well being Group has a model of the way you’ll be able to see this coming: One Well being.
Here’s the deal: this connectivity means there is an opportunity for AI. You see, health threats are not simply related to pathogens and disease, but also to the environment, to animal numbers, to weather patterns, to what we do. Is this something technology could address? It is!
But, wait. More advanced technology requires more stewardship. The money and data you collect from many organizations, countries and communities will result in choices that have a lot of social and economic implications.
As systems get more interconnected, being transparent and accountable becomes that much more essential.
How do you think we should address these issues?
Measuring impact instead of measuring technology
One of the easiest mistakes NGOs can make is to measure digital progress by counting tools.
- A chatbot launched.
- A dashboard created.
- An AI model integrated.
- A new platform adopted.
- None of those things automatically represent better health.
- The better questions are much more practical.
- Is the programme reaching more people?
- Are health workers saving time?
- Are patients receiving information faster?
- Are disease risks being identified earlier?
- Are errors decreasing?
- Are underserved communities being included?
- Is the technology affordable enough to maintain?
- Do communities trust it?
These are the measures that matter.
An AI system should earn its place in a health programme by improving something meaningful.
Sometimes the best decision may be not to use AI
Ever get the sense that we’re rushing to throw AI at every health problem? Well, actually-sometimes all you need is a database.
You could also ask whether people should be better trained to solve the problems without using complicated algorithms. Or perhaps the real answer is just to get more health workers on the ground.
Pause for a moment and consider if an optimized supply chain could give you more, even more, than the newest algorithm? Also, never forget – a public meeting can reveal data insights that no dataset ever will.
Lured in by the latest gadgets but always mindful of the obligation to “select the right [solution]” (WHO’s words) the WHO advice today serve as a reminder that proportion and the no-harm principle should be guiding star in decisionmaking. Responsible innovation – when to let the technology go and when to rein it in? What do you think?
Building a human-centred approach to AI in global health
Want to know how other NGOs can apply AI responsibly? Here are some practical principles that can really help. As a first step, you need to work on the health problem and not the technology.
It is important to test and validate the system with the actual end-users involved in the development.
Are you engaging with the local NGOs and communities? You should.
And this protecting sensitive health data is always a must. And always test in the real world don’t assume that because it’s working now, it’ll still be accurate down the road. Keep humans in the loop and hold systems to account in the real world. All of these simple steps can help keep you out of trouble and stick to our most important goal in global health improving lives.
The future of AI in global health will depend on trust
AI has genuine potential to improve global health.
It could help researchers work faster, strengthen public-health surveillance, support health workers, improve access to information and make some health services more efficient.
WHO recognizes these opportunities while also stressing that ethics and human rights need to remain central. Its 2025 guidance on large multi-modal models shows that governance discussions are continuing as AI systems become more capable and widely used in health contexts.
For NGOs, however, the biggest challenge may not be learning how to use AI.
It may be learning how to use it without losing the things that make health programmes effective in the first place.
- Trust.
- Local knowledge.
- Human judgement.
- Privacy.
- Compassion.
- Accountability.
A community health worker may not have access to the most advanced technology in the world, but they may know the community better than any algorithm ever could.
A doctor may question a recommendation because they understand the patient’s circumstances.
A local NGO may notice that a health programme is failing because the technology does not fit the community.
Those forms of knowledge matter.
Conclusion: AI should strengthen global health, not replace its human side
The conversation about AI in global health is sometimes presented as a choice between technology and traditional healthcare.
It does not have to be.
AI can process enormous amounts of information.
- It can identify patterns.
- It can help researchers.
- It can support disease surveillance.
- It can assist health workers.
- It can improve certain administrative and operational processes.
But it cannot carry the full responsibility for a person’s health.
That responsibility still belongs to people and institutions.
For NGOs, this means the most important question is not how quickly they can adopt AI.
It is whether they can adopt it responsibly.
The strongest global-health systems of the future may be the ones that combine technological capability with local knowledge and human judgement. They will use AI where it genuinely helps, protect communities from unnecessary risks and make sure that important decisions remain open to human review.
Because health is ultimately not just about data.
It is about people.
And as AI becomes a bigger part of global health, keeping those people at the centre should remain the most important measure of progress.
