Artificial intelligence (AI) has revolutionised healthcare by offering innovative solutions that address some of the most pressing challenges across the globe, particularly in developing nations. While developed countries lead the charge in AI integration, its real potential is realised when healthcare disparities in regions that need it most are bridged. Where access to healthcare is restricted and inequalities are vast, AI provides equitable healthcare solutions to close these gaps.

Understanding the healthcare gap in developing nations

A five-stage model can be conceptualised to define the progression of a nation in achieving what is called AI supremacy. This model outlines the journey from initial awareness and exploration to full integration and innovation in public health systems. Here's what the stages could look like:

This is the model that provides a framework for developing nations to follow as they concentrate their efforts on fully leveraging AI for public health. Most developing nations are currently between Stages 1 and 2, with some advanced regions approaching Stage 3. Developed nations have evolved and are easily in Stages 4 and 5.

SWOT analysis of AI in global health for developing nations

To provide a much better understanding of AI's potential and challenges in developing nations, I present a critical review of its current status through a SWOT analysis.¹ This key management tool provides insights to organisations involved in making informed decisions by focusing on strengths, mitigating threats, and taking maximum advantage of available opportunities.

Strengths

Vast and emerging talent pool

Specialised courses like Data Science and Machine Learning are being offered by a growing number of universities, to tap into the increasing pool of engineering talent.

Freedom from legacy systems

Older, inefficient systems and processes can be replaced with recent advancements in computing power and software to create an AI-based superior ecosystem.

Availability of big data

Developing nations constitute 90% of the world's population, with the share of patients requiring imaging during treatment being 95%. The amount of data generated is enormous which serves as a gold mine for developing AI-based healthcare applications.²

Weaknesses³

Technological barriers

It is no secret that developing nations lack the fundamentals for setting an efficient tech-driven environment. These primarily include the necessary infrastructure, such as reliable internet and electricity, to fully implement AI technologies.

High costs

Adoption of AI technology can be restricted due to the prohibitive financial investment, considering the country’s GDP share and budget allocations.

Data privacy concerns

With regulatory frameworks being less stringent, the level of data protection for vast datasets is always a matter of concern, raising questions about reliance.

Cultural resistance

Strong cultural ties can most often lead to opposition due to a preference for conventional medical procedures, making community engagement imperative.

Opportunities

Bridging the rural-urban divide

AI has the potential to significantly reduce healthcare disparities between rural and urban areas.

Scaling up preventive healthcare

AI can enable early detection and intervention, reducing mortality rates from preventable diseases.

Supporting global health goals

AI can contribute to achieving health-related Sustainable Development Goals (SDGs) by improving access to quality healthcare.

Innovative public-private partnerships

Public-private partnerships can help make AI technologies more accessible in developing nations.

Threats

Ethical and regulatory challenges

The use of AI raises ethical concerns, including algorithmic bias and data misuse, which could exacerbate inequalities.^3,4

Dependence on external technologies

Developing nations can become overly reliant on external funding and foreign-based AI resources, overlooking efforts to take control over healthcare systems.

Potential job displacement

The AI revolution’s potency to displace lower-skilled labour is a hard pill to swallow. This poses a challenge to governments, demanding careful management to ensure AI complements rather than replaces human labour.

Inequality in AI access

If AI is only accessible to certain populations, it could widen the existing healthcare gap rather than close it.

The role of AI in bridging the healthcare gap

AI’s superpower is its ability to process vast amounts of data, identify patterns, and generate actionable insights that ultimately aid in addressing the healthcare challenges developing nations face, particularly in rural areas. By harnessing AI, these countries can overcome many of the barriers that have traditionally hindered healthcare access and quality.

Enhancing diagnostic capabilities in rural areas

One important contribution of AI in rural healthcare has been the deployment of advanced diagnostic machinery, enhancing the quality of health services. Often the socioeconomic factors of rural populations put them at a disadvantage for receiving optimised healthcare facilities, which is even more pronounced in developing nations. In particular, Asia and Africa are home to 90% of the global rural population, and the inequitable distribution of healthcare personnel between rural and urban regions further compounds these challenges.⁵

AI-powered diagnostic tools have drawn attention in the recent past by addressing some of these problems. For instance, rural areas of India were introduced to the Early Detection and Prevention System (EDPS) in 1998 to enhance diagnostics in clinics struck by physician deficits. Across 933 patients, this tool demonstrated an outstanding 94% concordance with physician diagnoses.⁵

China, on the other hand, has taken this a notch up by deploying portable diagnostic machine stations in village healthcare facilities. This move has revolutionised diagnostics by enabling the conduction of multiple tests, including blood pressure, electrocardiographs, and routine urine and blood analyses. Additionally, many AI-driven innovative clinics have sought the attention of major technology firms in China.

In South Africa, a company called Medsol AI Solutions has come up with a cutting-edge ultrasound probe leveraged by AI that is specifically designed for rapid breast cancer detection. This is transformational in that the accompanying rapid diagnosis app facilitates quicker therapeutic interventions, and the portable nature of the probe extends screening services to regions with limited healthcare infrastructure.

Similarly, in Kenya, AI-driven remote cardiac diagnosis has made significant strides not only in reducing mortality rates but also in ensuring that only high-risk patients are transferred to tertiary hospitals.⁶

Expanding access to healthcare through telemedicine and mobile health applications

Telemedicine platforms are now AI-driven, enabling remote consultations that allow patients in isolated areas to receive medical advice without needing to travel. In India, a country with difficult terrain, AI-enabled platforms have improved maternal and child healthcare services, ensuring that even the most isolated communities receive timely care. 

At AstraZeneca, the A. Catalyst Network (A.CN) Africa Hub is driving critical health innovation by bringing entrepreneurs, start-ups, academics, governments, healthcare institutions, and other industries under one roof to achieve one goal: to ignite innovation that will secure better, more equitable health outcomes for patients.⁶

Optimising healthcare delivery and resource allocation

AI's healthcare data analysis feature also plays a crucial role in optimising healthcare delivery and resource allocation. For instance, AI models predicted disease outbreaks during COVID-19 by analysing population movements, historical disease trends, demographic information, and responses to treatment patterns. This predictive capability is crucial in regions prone to infectious disease outbreaks, where early intervention becomes instrumental in saving lives.

Addressing the impact of climate change on health

Public health and the health of the planet are inextricably linked, particularly in regions like Africa, where climate change has changed the equation disproportionately. AI-driven digital solutions that enable early detection and treatment of diseases can also help reduce the adverse environmental impact of healthcare. By addressing health issues earlier, the need for more resource-intensive treatments can be minimised, thereby reducing carbon emissions associated with healthcare.

Recommendations for enhancing AI integration in developing nations

To realise the potential of AI in healthcare in its entirety, the following recommendations are essential:^3,5

Invest in AI infrastructure and training

We will see governments and private organisations merging where possible to allocate resources to develop robust AI infrastructure in healthcare institutions, particularly in secondary and tertiary health centres. This, along with designing a comprehensive training curriculum for healthcare professionals, will enable the effective use of AI tools in delivering quality healthcare.

Promote ongoing education and collaboration

Staying updated with AI developments must be prioritised by healthcare professionals. Additionally, efforts have to be made to collaborate with healthcare providers and AI experts to encourage the development of an ecosystem where innovation thrives. This fosters an attitude that embraces a collaborative model, where AI complements rather than replaces clinicians.

Advocate for AI in healthcare education

Educational institutions and governments should advocate for the integration of AI in healthcare education. Introducing AI concepts early in the curricula will not only provide sufficient time for medical, pharmacy, nursing, and paramedical students to master them but also a chance to work on their application in the real world. 

Establish international partnerships and knowledge transfer

Developing nations in Stages 2 and 3 must start looking out for opportunities to engage in international collaborations with countries in Stages 4 and 5. With an established AI-based technology base, high-income nations can support development by facilitating knowledge transfer, sharing resources, providing subsidies, and accelerating the integration of AI. 

Develop ethical guidelines and data governance frameworks

The only way for international medical associations and policymakers to prioritise patient safety and well-being in the context of AI is by enforcing adaptive regulations. Establishing stringent data governance and privacy regulations is imperative to protect patient information and foster trust in AI applications.

Summary

AI is the cornerstone of transformation in the healthcare landscape for developing nations, bridging the gap between rural and urban areas and assisting in achieving Sustainable Development Goals. By following the roadmap outlined in the five-stage progression model and by leveraging the strengths and opportunities identified in the SWOT analysis, policymakers can ensure that AI becomes a tool for improving health outcomes.

The future looks bright when healthcare systems not just emphasise access, but also focus on high-quality care for all. With the right strategies and collaborations, AI can help us build an ecosystem where everyone, regardless of location or income, has access to the healthcare they need.