On the Complexities of Enabling Demonstrated Consent

On the Complexities of Enabling Demonstrated Consent
Open Peer Commentaries
Panagiotis Alexiou, Joel Azzopardi, Claude Julien Bajada, Jean-Paul Ebejer, Gillian M. Martin, Nikolai Paul Pace
American Journal of Bioethics, 7 April 2025
Excerpt
Barnes et al. (Citation2025) introduce a novel vision for biobanking consent in their article “Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI.” Their concept of “demonstrated consent” leverages blockchain technology and non fungible tokens, along with large language models to enhance transparency and participant engagement. We put this novel framework in context of existing approaches, and highlight some key questions that arise.
The central question that Barnes et al. seek to address is the inherent limitations of the following two traditional consent models in biobanking, namely:

• Study-specific consent: A type of consent where individuals give permission for the use of their bio-samples and data to be used in a single, well defined, research project. While ethically robust, it is often impractical in the context of long-term biobanking due to the increased administrative burden and inflexibility.
• Broad consent: A type of consent where individuals allow their bio-samples and data to be used in future, sometimes unspecified, research projects, with few or no specific restrictions. Importantly, without the need to be re-contacted or consulted. This approach is more efficient, but can undermine the autonomy of participants by failing to provide sufficient information about future research uses. Broad consent needs to be paired with strategies of risk mitigation, and continuous provision of information to participants.

In response to these challenges, various alternative models have been proposed, including tiered informed consent (Tiffin Citation2018), meta-consent (Ploug and Holm Citation2016), and dynamic consent (Kaye et al. Citation2015; Budin-Ljøsne et al. Citation2017) These models try to increase the involvement of participants, but are vulnerable to issues similar to study-specific consent. Dynamic consent in particular, has gained traction as a means of allowing participants to dynamically update their consent preferences in real time, thus tailoring their participation to studies based on their preferences. Individuals however, have to constantly manage their consent, leading to potential choice overload and consent fatigue. The “demonstrated consent” model proposed by Barnes et al. aims to circumvent these issues by providing a secure, transparent, and easily accessible source of information, without requiring participants to continuously manage their preferences. The central contribution of this manuscript is the proposed integration of non-fungible tokens (NFTs) and large language models (LLMs) to tackle these issues…