How to innovate responsibly with AI in healthcare?

On 2 December 2025, the ‘AI and ethics in practice’ learning community was joined by the learning community ‘8 Caring Technology Principles’ of the King Baudouin Foundation and Thomas More-Licalab. The topic of the session was on responsible AI innovation in healthcare, more specifically on how AI can contribute to innovation in healthcare while safeguarding ethical values, care practices, and human dignity.

AI technology is everywhere nowadays and certainly also in the field of healthcare. But how is AI currently being used in healthcare? What use cases are ongoing? And how can we ensure AI technology is being used in a responsible, fair and acceptable way? The first part of the session focused on these questions with a keynote of Tom Braekeleers, founder of NEXXT.HEALTH, and a testimonial of Louise Berteloot, AI engineer at AZ Delta. 

Tom Braekeleirs, founder of NEXXT.HEALTH, gave a keynote on the opportunities and challenges of acceptable AI in healthcare. He focussed on the opportunities of AI technology in the field of healthcare, while also stressing the importance of AI as a means and not as an end. AI technology should support and improve healthcare. In order to do so, AI needs to be acceptable, focussing on many aspects such as ethical, legal, causal, explainable and more. These aspects are crucial for humans to trust AI technology. If you want to know more about acceptable AI, Tom has written the book ‘Acceptable AI. 7 dimensions of a human-centred AI evolution.’. You can order your copy of the book here. 

Louise Berteloot, AI engineer at AZ Delta, gave a testimonial on AZ Delta’s journey towards data-driven healthcare. She focused on the challenges and the opportunities when engineers meet physicians by focussing on several AI projects of AZ Delta. The collaboration between physicians and AI technologies has some challenges (e.g. interpretation of the AI model, integration into work practices, etc.). Her insights showed the potential of AI and the need to integrate AI into the work practices of physicians effectively. 

Afterwards the participants could experience 3 tools or methods in a workshop setting. The 3 tools or methods each support in their own particular way responsible AI innovation in healthcare. 

• AI blind spots in (health)care (by Willemien Laenens and Pieter Duysburgh of the Knowledge Centre Data & Society): A card set to help you anticipate and avoid harmful impacts when bringing AI into healthcare settings. By shining a light on blind spots, it empowers you to take proactive steps. Each card describes a single blind spot. Per individual card, you’ll get a key question, the category or categories to which the blind spot belongs to, a short description, a concrete example and some reflective questions. As a set, these cards help you identify and address potential risks of AI applications in (health)care. For further information, please contact info@data-en-maatschappij.ai. 
• 8 Caring Technology Principles (by Ingrid Adriaensen en Hilde Vandenhoudt of Thomas More-Licalab): In this interactive workshop, participants discovered the 8 Caring Technology Principles as a practical and inspiring framework for co-creation with citizens and patients. They explored how to involve end users meaningfully from the very beginning, so that AI innovations respond to real needs rather than assumptions. For further information, please contact hilde.vandenhoudt@thomasmore.be or ingrid.adriaensen@thomasmore.be. 

LASO methodology (by Melissa Wittens of imec-SMIT, Vrije Universiteit Brussel): AI projects in healthcare often prioritize technological novelty over the real needs of healthcare workers, leading to solutions that fail to deliver the promised impact. The LASO methodology addresses this challenge by offering a practical, participatory approach to help hospitals harness the potential of AI in a responsible manner. By bringing together frontline staff, hospital leadership and legal, ethical and technical experts to identify workplace challenges and explore how AI can address them, LASO helps de-risk AI innovation by ensuring that AI opportunities are practical, ethically responsible and aligned with both workplace realities and institutional priorities. For further information, please contact melissa.wittens@imec.be. 

The ‘AI and ethics in practice’ learning community comes together four times per year. Each session focuses on a topic related to AI and ethics: from how to develop a strategy on responsible AI to how to set up an ethics board and best practices for sustainable GenAI. This community is open to many domains and is not limited to the field of healthcare. If you want to share best practices and insights on responsible AI innovation, you can join this network by registering for one of the meet-ups of this learning community (keep an eye on our calendar) or by sending an email to info@data-en-maatschappij.ai. 

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