Integrating Participatory Research and Literature Review to Identify Social and Ethical Requirements for Responsible AI in Heart Failure Treatment

Conference Paper (2025)
Author(s)

Miriam Cabrita (SHINE 2Europe)

Natalia Machado (SHINE 2Europe)

Harm op den Akker (SHINE 2Europe)

Isabella Cina (European Heart Network)

Joan Perramon Llussa (Universitat Politécnica de Catalunya)

Saskia Haitjema ( University Medical Centre Utrecht)

Andreas Triantafyllidis (Centre for Research and Technology Hellas)

Stefan Buijsman (TU Delft - Technology, Policy and Management)

Carina Dantas (SHINE 2Europe)

Research Group
Ethics & Philosophy of Technology
DOI related publication
https://doi.org/10.1109/DPH66411.2025.11198049 Final published version
More Info
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Publication Year
2025
Language
English
Research Group
Ethics & Philosophy of Technology
Publisher
IEEE
ISBN (electronic)
9798331566746
Event
10th International Digital Public Health Conference, DPH 2025 (2025-07-24 - 2025-07-26), Madeira, Portugal
Page Views
31
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Abstract

The rapid advancement of Artificial Intelligence (AI) in healthcare raises significant social and ethical concerns, particularly in cardiovascular care, the leading cause of death worldwide. Addressing these challenges requires a multidisciplinary, multi-stakeholder approach to ensure the responsible development and implementation of AI-driven solutions. First, a literature review examined how key ethical principles - namely, autonomy, confidentiality, data privacy, and equal treatment - are integrated into AI applications for heart failure care. These findings informed an online workshop with patient representatives, clinicians, and experts on ethical, legal, and social issues to discuss ethical concerns and practical implications. A team of requirements engineers and digital health experts synthesized insights from the literature review and workshop, specifying an initial set of requirements. Following multiple iterations with a multidisciplinary review team, the final set of 25 requirements was established. These were translated into software or system specifications where applicable or used to define guidelines for real-world implementation. This study provides a structured approach to embedding ethical and social considerations into AI-driven healthcare solutions. Its methods and key requirements are transferable to other AI applications in public health, promoting responsible and equitable adoption.

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