Which countries or groups still have few AI tools, and why?

"In low- and middle-income countries (LMICs), the fields of medicine and public health grapple with numerous challenges that continue to hinder patients' access to healthcare services."

"However, this adoption is far from uniform, with low-income countries (LICs) trailing behind significantly."

"ranging from health literacy, screening, triaging, remote healthcare support, mental health support, multilingual capabilities, healthcare communication and documentation, medical training and education, and support for healthcare professionals."

"Doctors in LMICs offered a wider range of diagnoses than LLMs, likely reflecting a broader local epidemiology."

"Even when informing the LLM in the prompt that the clinical encounter was taking place within a specific LMIC, this issue persists."

"Whilst these may have given better responses, a large proportion of users (especially"

"In many LMIC settings, AI is entering educational systems already affected by unequal internet access, equipment shortages, limited digital infrastructure, restricted institutional budgets, insufficient faculty training, and fragmented governance."

"a capability divide, involving faculty preparedness, institutional governance, and AI literacy"

"restricted institutional budgets, insufficient faculty training, and fragmented governance"

"The most easily accessible GAIs with the lowest barriers to entry lag behind the frontier models in terms of their capability, availability or safety (Li et al., 2024; Cottier et al., 2024; Stanford University, 2025). As a result, users with limited incomes or competencies are much less likely to reap the benefits than seasoned early adopters."

"Alibudbud explains that to progress the country toward having high-speed internet access everywhere, policies should require internet service providers to deliver a minimum quality of service in all locations, including rural and low-income ones."

"Focusing on the most-used AI platform, ChatGPT, as of May 2025, visits per internet user in high-income countries was nearly 50 times that of low-income countries, 8 times that of lower-middle income countries, and 3 times that of upper-middle income countries."

"the Government AI Readiness Index shows that most low-income countries, especially in Sub-Saharan Africa, score far lower on AI preparedness"

"These differences are particularly salient between Global North and Global South contexts [21, 22], but they also manifest within nations along socio-economic, geographic, demographic, and educational lines."

"Measures that account for multiple platforms also find that in most Sub-Saharan African countries, less than 10 percent of the working-age population uses AI."

"Studies explicitly foregrounding equity, access and infrastructural constraints in relation to GenAI are comparatively scarce, particularly in under-represented regions such as Leba-non and the broader Global South [16, 28, 30, 31, 48, 49]."

"This is partly because data packages are prohibitively costly for many in poorer countries, where a data-only 5GB mobile package costs more than 15 percent of the average person's total budget."

"access barriers arise when some families cannot afford AI learning tools or when students lack access to the necessary technological facilities for tools like ChatGPT [9]."

"While substantial datasets exist for other languages spoken in high-income countries, there is a significant deficit for languages that are primarily spoken in low and middle-income countries."

"As of June 2025, high-income countries accounted for 77 percent of global data center capacity, middle-income countries for 23 percent, and low-income countries for less than 0.1 percent."

"Women in the Global South, who on average have less access to technology and STEM education due to systemic gender biases, risk being left further behind in an AI-driven economy"

"Map O.1 Very few low-income countries have published national AI strategies"

"A primary equity concern is the digital divide (OECD, 2024; Shabbir et al., 2024), where GenAI may either bridge or exacerbate educational inequalities. While AI tools can democratise learning, access remains limited in under-resourced regions."

"Figure 7.2 Mobile phone ownership remains limited in low-income countries"

"For the 6.8 billion people—83% of humanity—who live in low-income and developing countries, AI tools will need to be adapted to meet their needs."

"Shabbir et al. (2024) highlighted that AI can supplement resource constraints in developing countries, but infrastructure deficiencies and digital literacy gaps remain."

"Advanced AI models—largely developed in the United States and China—offer the ability to"

"Singh (2024) documented that most AI-enhanced education remains concentrated in urban centres, excluding marginalised communities."

"These AI models, however, require huge data centers and large amounts of complex computing power, using massive amounts of electricity and water—with implications for climate change."

"These systems are predominantly developed by companies based in the Global North, trained on datasets that over-represent Western languages, cultural contexts, and knowledge systems"

"AI solutions will need to be delivered through voice calls on basic mobile phones for those who cannot read or afford smartphones"

"Despite the promise of GenAI, the OECD (2024) noted that global policies lack actionable strategies to close these accessibility gaps"

"to invest in electricity generation and distribution; expand access to computing power; and improve the availability of local data."