Recherche

This report on “Mapping AI Governance in Health: From Global Regulatory Alignments to LMICs’ Policy Developments” represents a first step in HealthAI’s implementation of national and regional regulatory mechanisms to form a Global Regulatory Network. It also demonstrates our commitment to not leave the low resourced countries behind, as well as push for decentralization of regulatory processes to cultivate local innovation and trust. Through this report, we examined global AI governance policies developed by key international institutions through an interoperability lens, explored influential jurisdictions setting global regulatory trends and expectations, as well as presented country-specific analyses of four countries representing different regions, namely Africa, Latin America, Middle East and Asia, to offer diverse perspectives on the challenges and progress in the governance of AI in health.

AI has the potential to revolutionize healthcare, but thoughtful governance is essential. UNITE and HealthAI released their position paper, emphasizing the crucial role of parliamentarians in guiding the responsible use of AI in health.

Advances in data-driven technologies such as artificial intelligence (AI) are transforming the health sector at an unprecedented rate. AI is enabling significant progress in healthcare, public health research, and drug development. In addition, AI has the potential to address issues around the broader social determinants of health by increasing access to health services, wellness and lifestyle management, and enabling efficient health systems management. However, these advances also raise social, legal and ethical questions around the protection of personal information, equitable access to health care and patient safety, among others. It is therefore critical that South Africa develops and implements appropriate regulatory frameworks around the responsible use and governance of data and AI within the health sector. There is a substantial body of research and several global, multilateral and national policy frameworks that engage with critical policy issues in this field. In South Africa, the nature of these issues is complicated by high levels of poverty, a large disease burden and highly unequal resourcing and access to health services. This Topical Guide reviews the current policy and research environment, with a view to adopting a more inclusive, human rights-based approach to the use of AI and data for improving health outcomes. Specific recommendations include the development of national policies and strengthening key public interest institutions to: (1) explicitly recognise and protect the human rights of patients and practitioners, (2) address issues related to data quality and bias, (3) clarify mechanisms for ensuring safety and accountability in the context of automated decision-making, (4) explore ex-ante and ex-post approaches for strengthening transparency, (5) understand the impact of data-driven technologies on trust and patient-centred care, (6) recognise concerns about automation and the need for skills development amongst healthcare workers, (7) seek ways for aligning the governance of data whilst recognising potential risks of broad data integration, and (8) support existing work on language and translation for more user-centred AI systems.

This brief outlines a framework for conducting gender-responsive health research, emphasizing the importance of understanding how gender norms, roles, and power dynamics influence health determinants, behaviors, and outcomes. It highlights persistent gender data and capacity gaps, particularly the historical prioritization of male-centric data and the lack of gender analysis in health studies. The authors advocate for an intersectional, feminist approach that incorporates both qualitative and quantitative data, engages local civil society organizations, and addresses structural inequalities—especially in the context of emerging data sources like AI and precision medicine. The brief also explores challenges such as inconsistent terminology, limited training, tokenistic inclusion of gender, and the ethical complexities of collecting data on sexual orientation and gender identity. Through actionable recommendations, it encourages health researchers to reflect on data biases, collaborate with gender experts, and embed equity and change at the core of their research design and implementation.

In this section you will find relevant materials that were shared during the course "Responsible Artificial Intelligence in Health: Ethical Foundations, Governance and Practical Solutions" organized by Health AI.