---

Artificial intelligence (AI) is rapidly reshaping industries, with healthcare being one of the most affected sectors. AI’s ability to enhance diagnostic accuracy, optimize treatment plans, and improve access to healthcare services is revolutionizing healthcare systems in high-income and middle-income countries. However, low-income countries (LICs) are significantly lagging behind in adopting AI technologies. Despite the tremendous need for technological advancements in LICs, several challenges prevent them from harnessing AI’s full potential in healthcare.

The global AI adoption rate is increasing, but this growth is unevenly distributed, with high-income countries leading and LICs trailing far behind. These disparities are not only technological but also economic and social, exacerbating existing global inequalities. While there is significant literature on AI’s transformative potential in advanced economies, research often overlooks LICs, leading to a lack of inclusive discussions on AI’s role in development. This gap in the literature contradicts principles of global equity and distributive justice, which call for equal access to technology regardless of a country’s income level.

The Global AI Adoption Gap

Artificial intelligence is transforming healthcare across high-income countries, enabling significant advances in diagnostics, personalized medicine, and telemedicine. According to the World Economic Forum, over 60% of businesses in high-income countries have integrated AI in some form by 2023. In the healthcare sector, AI is now being used to enhance medical imaging, predict patient outcomes, and assist healthcare professionals in making informed decisions. In contrast, LICs lag significantly behind, with less than 10% of organizations adopting AI technologies.

This disparity is especially stark in healthcare. The World Health Organization (WHO) reports that 50% of the world’s population lacks access to essential healthcare services, with much of this population residing in LICs. The adoption of AI in healthcare could help mitigate some of these access issues, but without the necessary infrastructure, skilled workforce, and financial resources, LICs are at risk of being left further behind.

The Importance of AI in Healthcare for Low-Income Countries

Low-income countries face significant healthcare challenges, such as high disease burdens, limited access to healthcare professionals, and inadequate health infrastructure. These challenges make AI especially valuable for LICs, as it offers a way to overcome some of these obstacles and improve healthcare delivery. AI can play a critical role in LIC healthcare systems by improving diagnostics, enabling disease surveillance, and expanding access to quality care through telemedicine.

Some of the key benefits AI can bring to LICs in healthcare include:

1. Enhancing Diagnostics: LICs often face a shortage of trained healthcare professionals, particularly in rural areas. AI-powered diagnostic tools can help bridge this gap by analyzing medical images, such as X-rays, CT scans, and MRIs, with the same accuracy as trained radiologists. For example, in Kenya, AI systems have been used to diagnose tuberculosis by analyzing chest X-rays, enabling faster and more accurate diagnoses in areas where radiologists are scarce. Similarly, AI has been applied in sub-Saharan Africa to improve malaria diagnosis, with algorithms analyzing blood samples to detect the disease with high accuracy.
2. Disease Surveillance and Management: AI can analyze large datasets from healthcare systems, mobile health platforms, and public health organizations to predict disease outbreaks and optimize response strategies. AI models have already been used to track the spread of infectious diseases like malaria and dengue fever in parts of Africa and Asia. By analyzing environmental, demographic, and health data, AI can help LICs anticipate outbreaks and mobilize resources before diseases become widespread, improving both prevention and treatment efforts.
3. Telemedicine and AI-Powered Decision Support: AI can enable more efficient telemedicine platforms by providing real-time decision support to healthcare professionals. In LICs, where access to specialists is often limited, AI-powered systems can analyze patient data and recommend treatment plans based on established medical guidelines. This reduces the burden on healthcare workers and helps ensure patients receive quality care, even in remote or underserved regions.
4. Personalized Treatment Plans: AI can analyze patient data to develop personalized treatment plans tailored to individual needs. This is especially important for managing chronic diseases, such as diabetes and hypertension, which are becoming more prevalent in LICs. By tailoring treatments to each patient’s medical history, lifestyle, and genetic factors, AI can help improve outcomes and reduce healthcare costs.

Challenges to AI Adoption in Low-Income Countries

Despite AI’s transformative potential, LICs face significant barriers to adopting these technologies, particularly in healthcare. These challenges can be grouped into four broad categories: digital infrastructure, human capital, data availability, and ethical and regulatory concerns.

1. Digital Infrastructure: One of the most significant barriers to AI adoption in LICs is the lack of digital infrastructure. AI technologies rely on robust internet connectivity, cloud computing, and data-sharing platforms—resources that are scarce in many LICs. According to the International Telecommunication Union (ITU), only 19% of the population in LICs had access to the internet in 2021, compared to 87% in high-income countries. This digital divide makes it difficult for LICs to deploy AI systems that require real-time data processing and analysis.
2. Human Capital and Skills Gap: AI development and implementation require a skilled workforce, including data scientists, AI engineers, and healthcare professionals trained in using AI tools. However, many LICs face a shortage of professionals with these skills. UNESCO reports that less than 2% of graduates in LICs specialize in computer science or related fields, compared to nearly 10% in high-income countries. Without a trained workforce, it becomes difficult to build, maintain, and scale AI systems in healthcare.
3. Data Availability and Quality: AI systems rely on large volumes of high-quality data to function effectively. Unfortunately, many LICs lack the necessary data infrastructure to collect, store, and analyze healthcare data. Additionally, existing healthcare data in LICs may be incomplete, inconsistent, or inaccessible, further limiting the development of AI solutions. The absence of comprehensive health records and national databases hinders the ability of AI systems to deliver accurate diagnostics and predictive insights tailored to local healthcare needs.
4. Ethical and Regulatory Concerns: In many LICs, the regulatory frameworks governing AI are underdeveloped or nonexistent. This lack of regulation raises concerns about data privacy, algorithmic biases, and the ethical use of AI in healthcare. For example, algorithms trained on data from high-income countries may not be applicable in LICs, leading to inaccurate diagnoses or reinforcing existing healthcare inequalities. Additionally, LICs often lack the legal and institutional mechanisms to protect patient data, which is critical when implementing AI systems in healthcare.

Bridging the AI Gap: Pathways for AI Catch-Up in LICs

Despite these challenges, there are opportunities for LICs to catch up in AI adoption. By drawing insights from the concepts of leapfrogging and absorptive capacity, LICs can take two primary approaches to integrating AI into their healthcare systems:

1. Leapfrogging: Leapfrogging refers to the ability of LICs to skip traditional stages of technological development and adopt the latest AI solutions directly. For example, many African countries have leapfrogged traditional banking systems by adopting mobile payment platforms like M-Pesa. Similarly, LICs can leverage mobile health platforms and cloud-based AI tools to deliver healthcare services more efficiently. Rwanda is a prime example of this approach. The country has successfully implemented AI-powered diagnostic tools in its healthcare system, using mobile platforms to extend healthcare access to remote areas.
2. Absorptive Capacity: In LICs where digital infrastructure is less developed, building absorptive capacity may be a more realistic approach. This involves developing the ability to absorb, adapt, and implement existing AI technologies. LICs can strengthen their absorptive capacity by investing in digital infrastructure, developing human capital, and fostering collaborations with more advanced economies. Kenya and Ghana, for example, have made significant progress in this area by partnering with international organizations and private sector companies to build their AI capacities in healthcare.

Key AI Domains in Healthcare for LICs

Several AI domains hold particular promise for LICs in improving healthcare outcomes:

1. Medical Imaging and Diagnostics: AI can assist radiologists in LICs by analyzing medical images, such as X-rays and MRIs, more efficiently and at a lower cost. AI-powered diagnostic tools can help healthcare providers in rural areas diagnose diseases such as tuberculosis, pneumonia, and cancer with greater accuracy. This not only improves patient outcomes but also reduces the workload on overstretched healthcare professionals.
2. Predictive Analytics: AI can be used to predict patient outcomes and identify at-risk populations based on healthcare data. For example, AI models can analyze patient records to predict the likelihood of disease outbreaks, enabling healthcare providers to allocate resources more effectively. Predictive analytics can also help LICs manage public health crises, such as the COVID-19 pandemic, by forecasting infection rates and informing policy decisions.
3. Natural Language Processing (NLP): NLP technology can help bridge language barriers in healthcare by translating medical information into local languages. In LICs, where healthcare workers may speak different languages than their patients, NLP can improve communication and ensure that AI-driven healthcare tools are accessible to non-English-speaking populations.
4. AI in Telemedicine: AI-powered telemedicine platforms enable healthcare providers in LICs to deliver remote consultations and diagnoses to patients in underserved regions. By integrating AI into telemedicine systems, healthcare workers can offer more accurate diagnoses and treatment recommendations, improving the quality of care in regions where healthcare access is limited.

Addressing Ethical Concerns and Regulatory Challenges

Ethical and regulatory challenges surrounding AI implementation in LICs must be addressed to ensure that AI adoption is equitable and benefits all segments of the population. These challenges include concerns about data privacy, algorithmic biases, and the risk of deepening existing healthcare inequalities. Without strong governance frameworks, there is a danger that AI could reinforce social divides, as well as harm vulnerable populations.

One key concern is algorithmic bias. If AI systems are trained on data from high-income countries, they may not perform well in LICs, where the demographics, disease prevalence, and healthcare infrastructure are different. To mitigate this risk, LICs need to develop localized datasets that reflect their healthcare challenges and patient populations.

Data privacy is another major concern. In LICs where legal protections for personal data are often weak, there is a risk that AI systems could infringe on patients’ privacy. Governments and international organizations must work together to establish regulatory frameworks that protect patient data while allowing for the responsible use of AI in healthcare.

International organizations like UNESCO, USAID, the World Bank, and the OECD can play a key role in addressing these challenges by providing technical support, funding, and capacity-building initiatives for LICs. These organizations can also help LICs develop ethical guidelines for AI implementation, ensuring that AI technologies are used in ways that are fair, transparent, and accountable.

Policy Recommendations: A Roadmap for AI in LICs

To ensure successful AI integration in healthcare, LICs need to adopt multi-faceted policy approaches:

1. Develop a National AI Strategy: LIC governments should craft comprehensive AI strategies that prioritize healthcare and other critical sectors. These strategies should outline goals for AI adoption, define regulatory frameworks, and establish public-private partnerships to promote AI development.
2. Invest in Digital Infrastructure: Governments and international organizations must work together to improve digital infrastructure in LICs. Investments in internet connectivity, cloud computing, and data-sharing platforms are essential for deploying AI systems in healthcare.
3. Foster Public-Private Collaborations: Partnerships between governments, private companies, and international organizations are essential to AI adoption in LICs. By collaborating with tech companies and global organizations, LICs can gain access to cutting-edge AI technologies and expertise.
4. Strengthen Human Capital: Developing a skilled workforce is crucial for AI adoption in LICs. Governments should invest in education and training programs that teach AI-related skills to healthcare professionals, data scientists, and engineers.
5. Promote Ethical AI Use: Governments must establish regulatory frameworks that ensure the ethical use of AI in healthcare. These frameworks should address concerns about data privacy, algorithmic bias, and the equitable distribution of AI technologies.

A Call for Global Equity in AI

Artificial intelligence holds the potential to revolutionize healthcare in low-income countries, offering solutions to some of the most pressing healthcare challenges in these regions. However, realizing this potential requires a concerted effort to overcome barriers related to infrastructure, human capital, and regulatory frameworks. By developing tailored strategies for AI adoption, fostering international collaboration, and addressing ethical concerns, LICs can harness AI’s transformative power to improve healthcare outcomes and reduce global health disparities.

Global equity in AI adoption is not just a technological issue; it is a matter of distributive justice. As the world becomes increasingly reliant on AI, it is essential to ensure that LICs are not left behind. The global community—governments, international organizations, and private companies—must work together to close the AI gap and ensure that all people, regardless of where they live, can benefit from AI-driven healthcare innovations.

---

Are you interested in how AI is changing healthcare? Subscribe to our newsletter, “PulsePoint,” for updates, insights, and trends on AI innovations in healthcare.