Round Table Discussion on AI, Healthcare, and Data Governance in Zambia

In this video, stakeholders in Zambia's healthcare, tech and law sectors discuss Artificial Intelligence and privacy concerns among other critical issues. Leading the discussion was SAIPAR’S Head of Legal Division Tinenenji Banda who recently wrote a policy brief which explores how AI-driven TB diagnostics could improve healthcare delivery while also raising important legal and governance questions around data protection and digital inclusion. Tuberculosis remains a leading cause of illness and death in Zambia, particularly in communities with limited access to healthcare services. Diagnosing TB typically requires trained radiologists and laboratory infrastructure, both of which are often scarce in rural or under-resourced areas. AI-powered diagnostic tools, particularly those that analyse chest X-rays, have the potential to bridge this gap. By automatically screening X-ray images and flagging potential TB cases, these systems can help clinicians detect the disease earlier, reduce diagnostic delays, and improve patient outcomes. In settings where medical specialists are limited, such technology can significantly extend the reach of healthcare services. Pilot initiatives using AI to support TB detection have already demonstrated encouraging results. Studies conducted in Lusaka have shown that AI systems can perform comparably to trained radiologists in identifying signs of TB from chest X-rays. Tools such as the qXR diagnostic system, for example, use machine learning algorithms to analyse lung images and identify patterns associated with tuberculosis. Importantly, these systems are not intended to replace doctors. Instead, they serve as decision-support tools that highlight suspicious cases and help clinicians prioritise patients who require further medical review. Beyond improving diagnosis, AI-assisted healthcare technologies also have broader implications for digital inclusion. By enabling faster and more accurate screening in remote areas, these tools can help ensure that people in underserved communities receive access to quality diagnostic services. This aligns with Zambia’s broader development goals of expanding equitable healthcare and harnessing digital technologies to improve public service delivery. However, while the technological promise is significant, the policy and legal framework governing data use has not yet fully caught up with these innovations. Zambia’s Data Protection Act of 2021 provides important safeguards for personal data, including requirements around consent, purpose limitation, and restrictions on cross-border data transfers. Yet the law was not designed with AI-enabled healthcare systems in mind. As a result, hospitals using AI-based diagnostic tools may encounter legal grey areas concerning how patient data can be processed, shared, or stored; especially if AI systems rely on cloud-based platforms or international data processing services. The policy brief highlights several key reforms that Zambia could consider. These include strengthening protections for health data, clarifying rules around the secondary use of anonymised medical data for AI training, and ensuring that patients are fully informed when AI systems are used in medical decision-making. It also recommends updating cross-border data transfer rules to reflect the global nature of modern AI systems, while maintaining strong safeguards for patient privacy and data security. Read the full policy brief here: https://saipar.org/assets/files/17727...