Leveraging responsible, explainable, and local artificial intelligence solutions for clinical public health in the global south
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Abstract
In the present paper, we will explore how artificial intelligence (AI) and big data analytics (BDA) can help address clinical public and global health needs in the Global South, leveraging and capitalizing on our experience with the “Africa-Canada Artificial Intelligence and Data Innovation Consortium” (ACADIC) Project in the Global South, and focusing on the ethical and regulatory challenges we had to face. “Clinical public health” can be defined as an interdisciplinary field, at the intersection of clinical medicine and public health, whilst “clinical global health” is the practice of clinical public health with a special focus on health issue management in resource-limited settings and contexts, including the Global South. As such, clinical public and global health represent vital approaches, instrumental in (i) applying a community/population perspective to clinical practice as well as a clinical lens to community/population health, (ii) identifying health needs both at the individual and community/population levels, (iii) systematically addressing the determinants of health, including the social and structural ones, (iv) reaching the goals of population’s health and well-being, especially of socially vulnerable, underserved communities, (v) better coordinating and integrating the delivery of healthcare provisions, (vi) strengthening health promotion, health protection, and health equity, and (vii) closing gender inequality and other (ethnic and socio-economic) disparities and gaps. Clinical public and global health are called to respond to the more pressing healthcare needs and challenges of our contemporary society, for which AI and BDA can help unlock new options and perspectives. In the aftermath of the still ongoing COVID-19 pandemic, the future trend of AI and BDA in the healthcare field will be devoted to building a more healthy, resilient society, able to face several challenges arising from globally networked hyper-risks, including ageing, multimorbidity, chronic disease accumulation, and climate change.
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Lack of representation in datasets during the development phase leads to models trained on biased datasets failing to generalize to underrepresented populations.
“Firstly, models trained on biased datasets fail to generalize to underrepresented populations.”
From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [35]
Section 3.5 (page not provided) says findings shaped largely by Global North research may not generalize to the Global South and calls for diverse, representative, locally relevant databases. This supports the concern about underrepresentation, but the citing sentence turns a possible limitation into a categorical claim that models trained on biased datasets fail to generalize. (LLM, conf=0.85)
Experimental — accuracy not yet measuredCitizenship intersects with race, ethnicity, and socioeconomic status, particularly for migrants and undocumented individuals, who often face additional systemic discrimination and healthcare exclusion.
“Similarly, citizenship intersects with race, ethnicity, and socioeconomic status, particularly for migrants and undocumented individuals, who often face additional systemic discrimination and healthcare exclusion, further underscoring the necessity of integrating an intersectional lens into AI design [35].”
From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [35]
Section 3.2 (page unavailable) identifies migrants, people without citizenship rights, racialized minorities, and socioeconomically deprived people as vulnerable groups whose needs are often overlooked in health policy. It does not specifically say that citizenship intersects with race, ethnicity, and socioeconomic status, or that undocumented individuals face additional discrimination and healthcare exclusion. (LLM, conf=0.82)
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Self-citationCené, C. W., Haymore, L. B., Enga, Z., Hodge-Sallah, S., Ritchwood, T. D., Wynn, M. R., Ellis, D., & Corbie-Smith, G. M. (2015) · Progress in community health partnerships · doi:10.1353/cpr.2015.0057 · Cited by 14 · see citing works
Redesigning public health for planetary health
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Citizenship Matters: Non-Citizen COVID-19 Mortality Disparities in New York and Los Angeles
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Monkeypox Prevention and Protecting Sex Workers: A Call to Action
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The COVID-19 Pandemic Seen from a Syndemic Perspective: The LGBTQIA2SP+ Community
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Self-citationRestructuring the Healthcare System to Protect Healthcare Personnel Amidst the COVID-19 Pandemic
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Trainee Perspectives Regarding Advanced Clinical Global Health Fellowships in North America
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Inclusive health: modeling COVID-19 in correctional facilities and communities
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Creating a continuum. The goal is to provide an integrated system of care.
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The importance of citizenship for deserving COVID-19 treatment
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Practical steps in promoting synergies between clinical medicine and public health
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Defining Clinical Public Health
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COVID-19: un’infezione gender-related? Possibili interpretazioni e limiti delle conoscenze
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A plea to merge clinical and public health practices: reasons and consequences
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Self-citation
Flagged first, then by citations
7 of the 114 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
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Applied artificial intelligence for global child health: Addressing biases and barriers
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Artificial intelligence and the politics of avoidance in global health
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Explorable Explainable AI: Improving AI Understanding for Community Health Workers in India
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Self-citationAI for Smart Disaster Resilience among Communities
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“…citizenship in-tersects with race, ethnicity, and socioeconomic status, particularly for migrants and undocumented individuals, who often face additional systemic discrimination and healthcare exclusion, further underscoring the necessity of integrating an intersectional lens into AI design [35].”
InfluentialReframing Justice in Healthcare AI: An Ubuntu‐Based Approach for Africa
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Towards an Inclusive Data Governance Policy for the Use of Artificial Intelligence in AfricaPreprint
Effoduh, J. O., Akpudo, U. E., & Kong, J. D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4581619 · Cited by 1 · see citing works
Self-citationHarnessing AI in Physical Therapy Modalities
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Leveraging AI and Big Data for Predictive Healthcare in Public Administration in Malaysia
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Case Studies in the Practice of Responsible AI for Development
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Applied informatics and health equity: challenges and strategies in the Global South
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Who Controls the Algorithm? Data Sovereignty, Digital Extractivism, and AI Governance Gaps in AfricaPreprint
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AI for Underserved Communities
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Explainable and Trustworthy AI in Oncology: A Systematic Review Across Major Organ Systems
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Whose Data, Whose Decisions? Ethical AI, Equity, and Epidemic Preparedness
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- Authors
- Kong, J. D., Akpudo, U. E., Effoduh, J. O., & Bragazzi, N. L.
- Affiliations
- York University (CA)
- Corresponding author
- Kong, Jude Dzevela
- Bragazzi, Nicola Luigi
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2023-02-04
- Volume
- 11
- Issue
- 4
- Pages
- 457
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC9956248
- OpenAlex
- W4319298252
- Cited by
- 36
- References
- 114
- Citations by year
- 2026: 15 · 2025: 11 · 2024: 8 · 2023: 2
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- Funders
- Canada’s International Development Research Centre (IDRC) (109981, 109559-001, RGPIN-2022-04559, DGECR-2022-00454, NFRFE-2021-00879)
- Swedish International Development Cooperation Agency (SIDA) (109981, 109559-001, RGPIN-2022-04559, DGECR-2022-00454, NFRFE-2021-00879)
- NSERC Discovery Grant (109981, 109559-001, RGPIN-2022-04559, DGECR-2022-00454, NFRFE-2021-00879)
- NSERC Discovery Launch Supplement (109981, 109559-001, RGPIN-2022-04559, DGECR-2022-00454, NFRFE-2021-00879)
- New Frontier in Research Fund- Exploratory (109981, 109559-001, RGPIN-2022-04559, DGECR-2022-00454, NFRFE-2021-00879)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, Global South, explainable AI, responsible AI, big data analytics, AI ethics, AI regulation, resource-limited settings, health equity, underserved populations, COVID-19 pandemic, health disparities
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