Artificial intelligence (AI) is transforming the landscape of healthcare by improving diagnostics, treatment, and patient outcomes. However, a recent study published in Digital Health sheds light on the significant disparity in access to AI technologies between the Global North and the Global South.
The research, conducted by scientists from the University of Sharjah in collaboration with U.S. researchers, highlights the concentration of AI advancements in high-income regions, leaving low- to middle-income countries at a disadvantage. While machine learning and robotics are revolutionizing healthcare in wealthier nations, their adoption in the Global South is limited.
The study underscores the existence of a healthcare divide between the Global North and the Global South, hindering the path to equitable access to quality healthcare. It emphasizes the need to bridge this gap to ensure affordable and effective healthcare for all populations.
The researchers conducted an integrative scoping review to identify recent studies from 2022 to 2025 that describe the challenges and contributions of AI health applications in the Global South. They found that while AI has the potential to improve disease tracking, expand access to healthcare services, support telemedicine, and advance preventive care models, several barriers impede its widespread implementation in less developed regions.
Key barriers identified in the study include poor infrastructure, data biases from Global North-centric AI models, limited local expertise, economic constraints, lack of biotech partnerships, and inadequate regulation. These challenges hinder the effective deployment of AI in improving healthcare for underserved populations in the Global South.
Dr. Syed Hussain, the lead author of the study, highlights the data bias in AI systems trained on datasets from high-income countries, leading to incorrect results when applied to diverse populations in the Global South. He also points out the lack of reliable internet, electricity, and skilled workforce in low-resource settings, making it challenging to develop and maintain AI systems.
The study advocates for more equitable partnerships between high-income nations and the Global South to foster technology sharing and capacity building. Dr. Hussain emphasizes the need for AI systems developed in high-income countries to adapt to local contexts in the Global South, considering disease patterns, infrastructural constraints, and cultural factors.
In conclusion, the study underscores the importance of digitizing health data, addressing data biases, ensuring data security, and promoting equitable collaborations to drive the adoption of AI in healthcare in the Global South. By overcoming these barriers and fostering genuine partnerships, the Global South can harness the potential of AI to revolutionize healthcare and promote equity within healthcare systems.
