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Trustworthy data for trustworthy AI
Trustworthy AI in healthcare is not decided at the moment a model is deployed. It is shaped much earlier, in how data is captured, prepared, governed and reused across clinical and research environments, and in whether organisations can understand, explain and take responsibility for those decisions over time.
This programme focuses on the operational foundations of trust in real-world health systems. Rather than revisiting high-level AI strategies or abstract policy debates, the sessions examine where trust breaks in practice: opaque data preparation steps, hidden exclusions in clinical data, governance models that do not scale beyond pilots, and organisational gaps that only surface once AI systems influence care.
The discussions intentionally span different healthcare realities. In highly regulated and digitally mature systems, trust often erodes through complexity: fragmented pipelines, accumulated technical debt and accountability that is hard to trace. In more resource-constrained settings, trust is challenged by different pressures: limited infrastructure, external dependencies and the difficulty of sustaining governance capacity over time. Seen together, these contexts expose the same underlying question: how do health systems build data practices that remain trustworthy under real constraints?
Opening remarks: Trustworthy Data for Trustworthy AI
Lecturer: Dmitry Etin
Verifiability as the source of trust in clinical AI
Lecturer: Kevin Groot Lipman
In this talk we will dive deeper in the different levels of verifiability and how this can guide us in knowing what AI output to trust. Moreover, we will discuss what level of evidence we want to ask for different kinds of AI during the clinical-decision making process.
Trust, Sovereignty, and Secure Use of Health Data
Lecturers: Carlos Sousa, Tiago Taveira Gomes
Healthcare is becoming increasingly dependent on advanced analytics, AI models, precision medicine, and cross-institution collaboration. Yet hospitals and governments face several growing challenges.
These issues slow down innovation, raise compliance costs, and create real risks for patient privacy and institutional trust. It also makes it difficult to modernize, to use AI, and to work together across institutions.
When AI Gets it Wrong: Why Responsible AI is Healthcare’s Life Support System
Lecturer: Elia Lima-Walton, MD, PA
Presentation reviewing the importance of trustworthy AI in healthcare. Mechanisms for trust and improved compliance in delivering care.
What is needed, why, and how to implement safely.
From Data Sharing to Data Insights: How Data Spaces Enable Clinical Insights in Dementia Care
Lecturer: Dmitry Etin
This session builds on that experience and looks ahead to AID-CARE, a newly starting international initiative led from Luxembourg, involving healthcare providers, academic centres and industry partners across Europe and Japan. Core contributors include the Luxembourg Institute of Health, Hôpitaux Robert Schuman, and Japanese NTT Data, working together to address dementia and neurodegenerative disease through data and AI. This domain relies heavily on unstructured clinical information, longitudinal patient trajectories, and multidisciplinary judgement, making it a demanding test case for turning shared data into clinically trustworthy insights and AI-supported decision-making.
Evidence‑Based Data for Evidence‑Based AI
Lecturers: Michael Bouzinier, Dmitry Etin, Scott Yockel
AI‑powered tools in hospitals are moving from pilots to operational use, rapidly changing the questions being asked by oversight bodies, regulators and the general public.
In this session we take that a step further and introduce a complementary approach to trust: instead of reconstructing data histories from documents, we can capture transformations automatically as workflows execute, creating structured, queryable records. Instead of manual compliance checks, we can express key requirements as formal, verifiable rules that run over those records. Drawing on real‑world implementations in large‑scale claims data pipelines (including Medicare) and trusted research environments, we will show how this approach helps clinical and programme leaders understand what was actually done to data, not just what should have been done, enabling them to approve deployments with genuine confidence and scale AI responsibly.
Strengthening health data governance legislative frameworks: The foundation of trusted digital health and AI
Lecturer: Mathilde Forslund
The health sector is undergoing rapid transformation driven by artificial intelligence and other digital technologies, which are now integral to health system delivery. Data underpins these systems and must be governed responsibly and equitably through strong legislative frameworks that improve health outcomes while protecting rights and upholding data sovereignty.
This presentation will look at Health data governance in the digital age, including how AI is changing the game. It will explore the need for action to strengthen national, health-specific legal and regulatory frameworks, as well as the role of regional and global cooperation to promote coherence and shared learning to support more secure, ethical, and equitable data and AI use, and foster responsible cross-border data use.
PANEL: Who Signs Off on AI in Healthcare
Moderator: Michel Silvestri
Panelists: Dmitry Etin, Elia Lima-Walton, MD, PA, Mathilde Forslund, Michael Bouzinier, Tiago Taveira Gomes
<< Navigate to Vitalis 2026 Conference highlights