AI in Healthcare Must Be Judged by Trust, Fairness and Governance-Not Just Accuracy

This blog is based on the chapter: Saatchi, A. G. (2026). The Role of Technology in Tackling

Global Health Challenges. In Addressing Global Health Challenges Through Financial Innovation and Health Technologies. IGI Global Scientific Publishing. Full chapter available here: Read the full chapter.

Artificial intelligence is increasingly being used in healthcare to support diagnostics, predictive analytics, disease surveillance, personalised medicine, and clinical decision-making. Its potential is significant. AI can analyse large datasets, detect patterns, support earlier intervention, and help stretched health systems use resources more effectively (Beam et al., 2023).

The chapter presents AI as one of the most transformative technologies in global health. It discusses how AI can improve diagnostic accuracy, support public health surveillance, and enable more personalised treatment strategies. One example cited is the international evaluation of an AI system for breast cancer screening, which demonstrated the potential of AI to improve early diagnosis (McKinney et al., 2020).

AI is also discussed in relation to disease surveillance. Predictive analytics can help forecast

outbreaks, inform public health planning, and support earlier allocation of resources. These tools can support faster and more anticipatory public health responses, especially when health systems are under pressure.

But AI should not be judged by performance alone. Accuracy matters, but it is not sufficient. AI systems are shaped by the data they are trained on, the assumptions embedded in their design, and the governance frameworks surrounding their use. If data are biased, incomplete, or unrepresentative, AI can reproduce or worsen existing inequalities.

This is why the chapter places strong emphasis on algorithmic bias, transparency, accountability, and equity. It draws attention to the risk that technologies described as neutral can reproduce discrimination when they are built on unequal data or deployed without community accountability. AI can support better decisions, but it can also hide harmful assumptions inside apparently objective systems (Dankwa-Mullan, 2024; Flores et al., 2024).

The chapter also engages with broader questions around data colonialism, decolonial frameworks, racialised surveillance and the politics of what gets measured in global health. It argues that digital tools are not neutral. They shape what becomes visible, what is counted, whose experiences are represented, and whose needs are overlooked. These questions matter because global health technologies are often designed, funded or interpreted far away from the communities whose data they rely on (Adams, 2016; Benjamin, 2019; Biruk, 2017; Browne, 2015).

The future of AI in health depends not just on technical progress, but on ethical maturity. If AI is to contribute meaningfully to global health, it must be governed in ways that protect people, include communities, and ensure that innovation serves the public good.

References

Adams, V. (2016). Metrics: What counts in global health. Duke University Press.

Beam, A. L., Drazen, J. M., Kohane, I. S., Leong, T. Y., Manrai, A. K., & Rubin, E. J. (2023). Artificial intelligence in medicine. The New England Journal of Medicine, 388(13), 1220–1221.

Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity Press.

Biruk, C. (2017). Cooking data: Culture and politics in an African research world. Duke University Press.

Browne, S. (2015). Dark matters: On the surveillance of Blackness. Duke University Press.

Dankwa-Mullan, I. (2024). Health equity and ethical considerations in using artificial intelligence in public health and medicine. Preventing Chronic Disease, 21, 240245.

Flores, L., Kim, S., & Young, S. D. (2024). Addressing bias in artificial intelligence for public health surveillance. Journal of Medical Ethics, 50(3), 190–194.

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., & Suleyman, M.(2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788),89–94.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

To explore the full argument, case studies and complete reference list, read Ameneh Ghazal

Saatchi’s chapter, The Role of Technology in Tackling Global Health Challenges, published in

Addressing Global Health Challenges Through Financial Innovation and Health Technologies

by IGI Global Scientific Publishing: Read the full chapter.