Missing Links: Health, Artificial Intelligence & the Global South

By Ilakkiah Chandran

The National Institutes of Health has committed to investing $130 million USD by 2026 in innovations focused on artificial intelligence (AI) to accelerate its application to biomedical research and healthcare.1 AI includes any tool that uses existing data and algorithms to effectively identify and solve problems in different systems. It has become more common in healthcare, whether as a diagnostic tool for chronic illnesses like diabetes, a rehabilitation tool, or cancer therapy. Researchers continue to look for ways to use AI in healthcare to improve access to healthcare, promote positive patient outcomes, and increase the healthcare system’s efficiency. Despite the growing acceptance of this in the Global North, (i.e., the developed world), the perspectives in the Global South (i.e., the developing world) are more skeptical.2

Clinicians and the general public in the Global South often view AI as an unfavourable tool that could compromise their safety, promote discrimination and exacerbate challenges to their day-to-day living.3 Since we perceive AI as effective and applicable in the Global North, it’s easy to overlook the fears and skepticism among the Global South as unjustified. Nevertheless, it’s crucial to consider the possible reasons behind this fear.

AI as New Colonialism

Firstly, AI has been described as “new colonialism” given its continued ability to learn from data and survey people and their activities.4 Colonialism and neocolonialism, the experience of having one’s experiences and spaces dominated by settlers, are firmly rooted realities for citizens of the Global South as they continue to face their consequences today.5 Having lost aspects of their culture, lifestyle and identities to colonization efforts has left long-term impacts and led to mistrust against the Global North.6 This isn’t new; during COVID-19, vaccine mistrust was highly prevalent amongst people in Sub-Saharan Africa as they connected their experiences of colonialism to vaccination.7 Since health-oriented AI often originates from the Global North, the ingrained mistrust of the North makes citizens of the South doubtful. Similarly, fears surrounding the potential of discrimination and oppression exist, given their experiences in the past.3

Failure to consider needs and training

Although you need a screwdriver, you only have a hammer. Sounds frustrating, right? Frontline health workers in the Global South are often put in similar situations where they are expected to effectively learn how to implement technology from the North with minimal training.8 For example, in rural India, implementing AI-enabled mobile health applications made community health workers feel the tool was ineffective and, in some cases, doubt their abilities as they had difficulties adapting to a system they were not trained in.9 The differences in medical training, familiarity with technology based on AI, needs, and perceptions are often neglected when implementing healthcare AI in the Global South.8 This challenges the implementation of AI and their current practices as they must allocate time and effort to learn tools that are not tailored to meet their needs.

Unsustainable Implementation

Thirdly, the implementation of AI in the Global South is not made to be sustainable.10 In many cases, healthcare AI is dropped off in these countries with the expectation of achieving the same success seen in the Global North.9 Even if patients and clinicians trust these tools and have adequate training to implement them, the health system must be prepared to sustain them. The implementation of health-based AI often neglects the role of the supply chain (i.e., available natural resources, production lines, and equipment) in building and ensuring the functioning of these tools.9 The failure to acknowledge the role of these factors in limiting the safe and responsible use of health AI is reflected in the lack of policies prioritizing the successful implementation of health AI in the Global South.10

The barriers that prevent the successful implementation of health-oriented AI in the Global South can be addressed with adequate consideration. Acknowledging the consequences of colonialism and neocolonialism on the Global South before implementing health-oriented AI can be the first step in successful translation. Preparing both the Global North and South for discussion of healthoriented AI can build the opportunity to address hesitations and perceivable challenges to improve its implementation and sustainability. Its applicability remains endless as we work towards a future that will inadvertently rely on AI for day-to-day tasks. Collaboratively working towards tackling these underlying concerns can ensure that everyone can benefit from this new era of health.

References

1. National Institutes of Health. NIH launches Bridge2AI program to expand the use of artificial intelligence in biomedical and behavioural research. National Institutes of Health, U.S. Department of Health and Human Services; 2022 [cited 2023 Apr 10]. Available from: https://www.nih.gov/news-events/news-releases/nih-launches-bridge2ai-program-expand-use-artificial-intelligence-biomedical-behavioral-research#:~:text=The%20National%20Institutes%20 of%20Health,biomedical%20and%20behavioral%20research%20 communities.

2. Wall PJ, Saxena D, Brown S. Artificial intelligence in the Global South (AI4D): Potential and risks. arXiv preprint arXiv:2108.10093. 2021 Aug 23.

3. Dubber MD, Pasquale F, Das S. AI and the Global South: Designing for Other Worlds. In: The Oxford Handbook of Ethics of Ai. Oxford: Oxford University Press; 2021.

4. Sahbaz U. Artificial intelligence and the risk of new colonialism. [Internet]. 2019 Jul 1(14):58-71. Available from: https://www.jstor. org/stable/48573727

5. Iyer L. Direct versus indirect colonial rule in India: Long-term consequences. The Review of Economics and Statistics. 2010 Nov 1;92(4):693-713.

6. Wietzke FB. Long-term consequences of colonial institutions and human capital investments: Sub-national evidence from Madagascar. World Development. 2015 Feb 1;66:293-307.

7. Mutombo PN, Fallah MP, Munodawafa D, Kabel A, Houeto D, Goronga T, Mweemba O, Balance G, Onya H, Kamba RS, Chipimo M, Kayembe JN, Akanmori B. COVID-19 vaccine hesitancy in Africa: a call to action. Lancet Glob Health. 2022 Mar;10(3):e320-e321. doi: 10.1016/S2214-109X(21)00563-5. Epub 2021 Dec 20.

8. Damoah IS, Ayakwah A, Tingbani I. Artificial intelligence (AI)-enhanced medical drones in the healthcare supply chain (HSC) for sustainability development: A case study. Journal of Cleaner Production. 2021 Dec 15;328:129598.

9. Okolo CT. Optimizing human-centered AI for healthcare in the Global South. Patterns. 2022 Jan 3:100421.

10. Naidoo S, Bottomley D, Naidoo M, et al. Artificial intelligence in healthcare: Proposals for policy development in South Africa. S Afr J Bioeth Law. 2022 Aug 5;15(1):11-16.