AI in Global Healthcare: Who Benefits and Who Is Left Behind?

Pamela Moyo 

Health & Medical Writer 

Artificial intelligence (AI) is rapidly transforming healthcare systems worldwide. From  detecting disease in medical imaging to supporting virtual consultations, AI is  increasingly being positioned as a tool that could improve diagnosis, treatment and  access to care. 

Its potential has made it a growing focus for governments, healthcare providers and  organisations such as the World Health Organization. However, while AI is often  presented as a solution to global health challenges, its benefits are not being  experienced equally across all regions. 

This raises an important question: who is truly benefiting from AI in healthcare and  who is being left behind?

The Promise of AI in Healthcare 

AI has attracted significant attention because of its ability to improve efficiency and  support clinical decision-making. In areas such as medical imaging, AI systems have  demonstrated the potential to assist with earlier detection of conditions such as  cancer, which can improve patient outcomes [1]. 

Beyond diagnosis, AI is also being used to streamline administrative processes,  optimise resource allocation and support healthcare professionals in high-demand  systems. These improvements can reduce waiting times and increase overall  efficiency. 

AI also offers opportunities to expand access to healthcare. Digital health platforms  and telemedicine allow patients to access care remotely, which could help bridge  gaps in underserved areas. AI is also being explored in global health initiatives,  including disease surveillance and vaccination programmes [2]. 

However, these benefits depend on strong infrastructure, reliable data systems and  sustained investment conditions that are not equally present across all countries.

Inequality in AI Implementation 

Despite its potential, AI is not being implemented evenly across global healthcare  systems. Its effectiveness depends on access to digital infrastructure, high quality  data and well-resourced healthcare systems. 

In high income countries, these systems are often already in place, allowing AI  technologies to be integrated more effectively. In contrast, many low and middle income countries face challenges such as limited internet access, gaps in data  collection and under-resourced healthcare systems. 

These differences can limit the impact of AI-driven healthcare solutions. 

For example, AI has been explored as a tool to improve vaccine distribution and  predict outbreaks. However, the success of these systems depends on consistent  data and stable healthcare infrastructure. In settings where these conditions are  lacking, the benefits of AI may be significantly reduced. 

This reflects what is often described as the digital divide, where differences in access  to technology shape outcomes across regions [3]. As a result, AI may not reduce  inequalities but instead reinforce existing ones.

Ethical Considerations: Data and Representation 

AI systems rely heavily on data. However, the datasets used to train these systems  are not always globally representative and are often based on populations in high income countries [4]. 

This creates a risk of bias. If AI systems are trained on limited or unrepresentative  data, their outputs may not accurately reflect the needs of diverse populations. This  can lead to unequal healthcare outcomes and reinforce existing disparities. 

There are also concerns about transparency and inclusion. Many AI systems are  developed and implemented without meaningful engagement from the communities  most affected. This can contribute to mistrust, particularly in healthcare systems that  are already under pressure. 

Ethically, the issue is not just how AI works, but whose data is used, whose voices  are included and who benefits. 

Policy and Accountability Challenges 

AI is advancing rapidly, but policy and regulatory frameworks have struggled to keep  pace. This is a global issue, not limited to any one country. 

While organisations such as the United Nations and the World Health Organization  provide guidance, there is no single global authority responsible for regulating AI in  healthcare. 

In many cases, AI systems are developed by private companies, with varying levels  of oversight. This raises questions about accountability, particularly when systems  produce biased or inaccurate outcomes. 

Without clear governance and accountability structures, AI risks being implemented  faster than it can be safely regulated.

Financial and Global Governance Challenges 

The cost of implementing AI in healthcare is another important factor. Developing  and maintaining AI systems requires investment in infrastructure, data systems and  skilled professionals. 

High-income countries are often better positioned to absorb these costs, while lower income countries may face significant financial barriers. 

Past global health challenges, such as unequal access to vaccines during the  COVID-19 pandemic, demonstrate how cost and access can influence health  outcomes [5]. 

Governance is also fragmented. AI in healthcare is shaped by a combination of  global, regional and national organisations, including groups such as the Southern  African Development Community and the Association of Southeast Asian Nations. 

However, this multi-layered system does not provide a unified global standard,  raising questions about who sets ethical guidelines and how they are enforced.

Towards More Equitable AI in Healthcare 

Addressing these challenges requires a more coordinated and inclusive approach. 

One priority is improving the diversity and representation of datasets used in AI  systems. This would help ensure that outputs are more accurate across different  populations. 

Greater collaboration between international organisations, governments and  healthcare providers is also needed to align standards and reduce inconsistencies in  implementation. 

Investment in healthcare infrastructure is equally important. Without the necessary  systems in place, many countries will not be able to fully benefit from AI  technologies. 

Finally, transparency and public engagement should be prioritised. Involving  communities in discussions about AI in healthcare can help build trust and ensure  that these technologies meet real-world needs. 

Conclusion 

Artificial intelligence has the potential to transform healthcare globally. However, its  impact depends not only on technological innovation, but also on the systems that  support its use. 

As this article has shown, differences in data, infrastructure, funding and governance  shape how AI is implemented across regions. 

Without careful planning and coordination, AI risks reinforcing existing global health  inequalities rather than reducing them. 

Ultimately, the challenge is not whether AI can improve healthcare, but whether it  can do so equitably.

References 

1. Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare  Human Again. 2019. 

2. World Health Organization. Ethics and governance of AI for health. 2021. 3. World Bank. Digital Development Overview. 2022. 

4. Gebru T, et al. Datasheets for datasets. 2018. 

5. United Nations. COVID-19 global response reports. 2021.

Assessed and Endorsed by the MedReport Medical Review Board