A comprehensive study that maps how gender and racial bias can appear in biomedical AI, with examples from cardiovascular disease, depression, and diabetes. It highlights where these biases occur in data and algorithms, and explains how they can lead to unequal treatment of patients. Read the Report here

An electronic platform that collects evidence on gender and racial bias in biomedical AI. It allows users to query bias distribution and impact analysis. To ensure responsible use, access is restricted: professionals (such as healthcare staff, researchers, or CSO representatives) must declare their professional status before being granted entry by the administrators. The database is designed as a secure tool for healthcare, research, and civil society practice.
Find the Database here.

A practical framework that sets out how biomedical AI can be used in line with the EU Charter of Fundamental Rights. It integrates key principles such as human dignity, equality, non-discrimination, and the right to health. The model will guide hospitals, regulators, and civil society organisations on how to keep AI transparent, fair, and accountable. It will be available electronically in English and seven partner languages.
Access the Model here

A handbook created for healthcare professionals in public hospitals. It explains how to critically use AI tools in diagnosis and treatment, showing how to spot biases and how to apply the regulatory model in daily practice. The guidebook builds directly on the project’s training workshops and will be available in eight languages to maximise usability.
Read the Guidebook here

A handbook tailored to civil society organisations. It explains the basics of biomedical AI, the risks of bias, and offers strategies for raising awareness, advocating for equality, and supporting citizens. It is designed as a practical tool that NGOs and rights-based organisations can use in their activities, available in multiple languages.
Read the Guidebook here.

A European network of hospitals and civil society organisations committed to ensuring fairness in AI. By the end of the project, 184 organisations will have joined and received the “AEQUITAS badge.” Their profiles will be published on the project website as part of a visible and lasting community of practice.
Find the Network here.

A reflective report that gathers the experiences of the project, highlighting what worked well, what challenges were faced, and what lessons can guide future initiatives.
Read the Report here.

Short but targeted publications for policymakers and regulators at national and EU level. It sets out concrete, actionable steps to make AI in healthcare more transparent, fair, and rights-respecting. It will be available electronically in English and seven other languages.
Find the Policy Recommendations here.