The AI + Society Initiative is excited to continue amplifying the voices of emerging scholars from around the world through our bilingual blog series Global Emerging Voices in AI and Society, stemming from the Global AI & Regulation Emerging Scholars Workshops and the Shaping AI for Just Futures conference. This series showcases the innovative research and insightful discoveries presented during these events–and beyond–to facilitate broad knowledge sharing, and engage a global network in meaningful interdisciplinary dialogue.
The following blog is authored by Renan Gadoni Canaan building on the poster, co-authored with Matheus Falcão, “Governance of Health Data for AI Innovation: Reconciling personal data protection, economic justice and non-discrimination in the context of Brazil” selected by an international committee as part of the poster session at the Shaping AI for Just Futures conference.
The “one-stop shop” model for health data access: Balancing AI innovation and risks in emerging economies
Imagine a doctor finishing a consultation without having to type a single note. Instead, an AI-powered tool records, summarizes, and integrates the conversation into an electronic medical record (EMR). This technology already exists. Known as “AI scribes,” it has reduced paperwork for healthcare professionals by 80%, allowing them to focus more on patient care.
This is just one example of how AI is rapidly transforming healthcare, offering immense potential to improve well-being. However, AI-powered tools rely on vast amounts of personal health information for training and operation. This raises critical concerns in many countries, particularly emerging economies.
Emerging economies face unique challenges when it comes to accessing health data for AI innovation. Unclear legal frameworks, reliance on foreign infrastructure, low public trust, and fragmented health data interoperability systems all contribute to these challenges. One promising model to address such issues comes from Finland, which provides a compelling example of balancing benefits and risks through its centralized “one-stop shops” for health data access. This model will be discussed below.
Navigating the benefits and risks of AI in healthcare
The AI scribes anecdote exemplifies the dual nature of AI-powered healthcare tools. By automating documentation, they free up time for doctors, enhance record accuracy, and alleviate stress in overwhelmed healthcare systems. However, these benefits come with risks, such as security, privacy concerns, and potential misuse. Additionally, errors in AI-generated summaries could contribute to misdiagnoses or biased recommendations.
A key reality behind these challenges is that companies rely on patient data to develop and improve AI tools. Access to large health datasets is crucial for AI innovation in healthcare. Beyond collecting new health data, it is also essential to repurpose existing data originally gathered for healthcare delivery. This practice, known as “secondary use,” enables the development of new AI applications. However, balancing the benefits of data access with privacy and security risks remains a challenge.
The challenges of health data Sharing in emerging economies: the case of Brazil
Many developing countries rely on public healthcare services that cover most of the population and share similar characteristics and challenges mentioned above. Brazil, a vast middle-income nation where 80% of the population depends on the public healthcare system, highlights these difficulties.
- Fragmented systems: Brazil’s Unified Health System (SUS) operates across federal, state, and municipal levels. This creates isolated data silos with inconsistent data collection standards, which in turn hinders interoperability and health data integration, and ultimately slows AI development in healthcare. For example, a patient in São Paulo receives primary healthcare at a municipal clinic and later needs emergency care at a federal university hospital. Each institution collects patient data using different, non-interoperable electronic health record systems, leaving the patient’s medical history fragmented. This lack of integration prevents machine learning models from accessing a comprehensive, high-quality dataset.
- Unclear legal frameworks: Brazil’s General Data Protection Law (LGPD) classifies health data as sensitive and requires explicit consent for its use, especially for secondary purposes. While exceptions exist for research and statistics, the absence of clear guidelines creates legal uncertainty, which may discourage innovators from developing AI solutions.
- Low public trust: There is low trust in public institutions. These sentiments limit citizens' willingness to engage and share health data for AI initiatives.
- Digital infrastructure: Brazil depends on foreign digital infrastructure providers, such as Amazon Web Services (AWS). This raises concerns about national data sovereignty. Limited domestic data infrastructure complicates the ability to control and use data generated within its borders for its own interests.
Addressing these challenges requires a governance model that ensures secure, transparent, and efficient health data access for AI innovation. Finland provides a compelling example of how to encourage access to health data for AI innovation while addressing risks to individuals. The next section explores Finland’s "one-stop shop" model and examines whether a similar framework could help countries like Brazil unlock the potential of health data while safeguarding public trust.
One-stop shops for health data: A model for secure and responsible governance
Through the Act on the Secondary Use of Health and Social Data (2019), Finland introduced Findata, a centralized government-run social and health data permit authority. It streamlines access to health data for research and innovation in a simplified and secure manner. Findata consolidates health data from multiple public sources and allows researchers and policymakers to access data through a controlled remote environment, where it is pseudonymized to protect privacy. This approach improves efficiency while maintaining strict governance principles.
Could countries like Brazil benefit from a similar approach?
Brazil’s universal healthcare system (SUS) and its expanding digital health infrastructure position it well for adopting similar governance mechanisms.
Establishing a centralized health data authority in Brazil could create legal certainty for AI-driven healthcare innovation by empowering the authority to set rules for access to data for secondary use. Additionally, a locally built health data permit authority could enhance national data sovereignty by reducing reliance on foreign service providers while improving interoperability through standardized health data protocols. Also, such a system would strengthen transparency by allowing individuals to opt out and participate in governance decisions. Most importantly, by balancing access to data for innovation while protecting citizens from potential risks, a centralized authority would play a crucial role in fostering public trust.
Challenges for implementation
Implementing such a model in Brazil will not be without obstacles. The country’s fragmented institutional landscape and significant digital inequality could hinder the effectiveness of a centralized system.
However, with the right investments and public engagement, Brazil - and other developing nations with emerging digital health infrastructures - could create a centralized health data system that fosters both innovation and trust. By learning from Findata’s experience, Brazil and other developing nations can shape a future where health data drives innovation while remaining a safeguarded public resource.
Key resources to learn more
Finland, Act on the Secondary Use of Health and Social Data, 552/2019.
Organisation for Economic Co-operation and Development (2023). Drivers of Trust in Public Institutions in Brazil Building Trust in Public Institutions, OECD Publishing, Paris.
Porto Júnior, Odélio et al., (2022). LGPD e uso Secundário de Dados de Saúde, A Year in Privacy, b/luz.
Scassa, Teresa and Kim, Daniel (2025). AI Medical Scribes: Addressing Privacy and AI Risks with an Emergent Solution to Primary Care Challenges, TMU Law Review.
AI Medical Scribes: Addressing Privacy and AI Risks with an Emergent Solution to Primary Care Challenges
Renan Gadoni Canaan is a Ph.D. Candidate at the University of Ottawa, Faculty of Law and a Scotiabank Student Fellow with the AI + Society Initiative. He was named an Emerging Leader in the Americas by the Canadian government through the ELAP program and is a former MITACS fellow at the Centre for Law, Technology and Society. He is currently pursuing his doctorate in Data Governance at the University of Ottawa. With a multidisciplinary background that perfectly aligns with research at the intersection of Law, Innovation, and Data Governance, Renan has a unique academic journey. He initially studied Science and Economics at the Federal University of Minas Gerais in Brazil. Later, he earned a Chancellor’s International Scholarship to join the Science and Technology Policies Program at the University of Sussex. His research contributes to the ongoing global discussion on two main topics: (i) the Governance of Health Data for AI Innovation and (ii) the post-colonial influence of Western Digital Regulations on the Global South.
This content is provided by the AI + Society Initiative to help amplify the conversation and research around the ethical, legal and societal implications of artificial intelligence. Opinions and errors are those of the author(s), and not of the AI + Society Initiative, the Centre for Law, Technology and Society, or the University of Ottawa.