Authors:
Ismael Jaggi
1
;
Rachele Carli
2
;
Berk Buzcu
1
;
3
;
Michael Schumacher
1
and
Davide Calvaresi
1
Affiliations:
1
University of Applied Sciences and Arts Western Switzerland (HES-SO Valais/Wallis), Sierre, Switzerland
;
2
UMEA University, UMEA, Sweden
;
3
The Sense Innovation and Research Center, Lausanne, Switzerland
Keyword(s):
Dynamic Informed Consent, Agent-Based Virtual Assistants, Healthcare, Data Protection.
Abstract:
The rapid adoption of personalized systems, driven by advancements in natural language processing, sensor technologies, and AI, has transformed the role of virtual personal assistants (VPAs), particularly in healthcare. While VPAs promise to enhance patient experiences through tailored support and adaptive workflows, their complexity often results in opaque functionalities hindering user understanding. This lack of transparency poses significant challenges, particularly in the context of informed consent, where users must comprehend the implications of sharing sensitive personal data. Existing consent systems often rely on static declarations and extensive documentation, which overwhelm users and fail to ensure informed decision-making. To address this problem, this paper presents a novel consent management approach integrated into the EREBOTSv3.0, an agent-based GDPR-compliant explainable framework for virtual assistants. The proposed solution introduces (i) an interactive method th
at structures consent into clear sections with summaries and examples to improve user comprehension and (ii) a question-based verification mechanism that assesses understanding and reinforces knowledge when needed. By leveraging EREBOTS’ modular architecture, real-time feedback, and secure data management, the proposed approach enhances transparency, fosters trust, and simplifies the consent understanding for dialog-based healthcare systems. This work lays the foundation for addressing critical challenges at the intersection of personalized AI, healthcare, and data protection.
(More)