Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI.

Area of Science:

• Biomedical Ethics
• Blockchain Technology
• Artificial Intelligence

Background:

• Ensuring informed consent for biological sample donation in research is challenging.
• Current consent models struggle to adequately inform donors about future sample uses.
• Lack of transparency can undermine voluntary participation in research.

Purpose of the Study:

• To propose a novel consent framework called "demonstrated consent" for biological sample donation.
• To leverage blockchain and generative AI to improve transparency and donor information.
• To address the limitations of traditional consent models in research.

Main Methods:

• Utilizing blockchain technology to create unique non-fungible tokens (NFTs) for each donated sample.
• Storing metadata on NFTs detailing planned and past research uses of the biological samples.
• Employing a customized large language model (LLM) to present complex usage data interactively and understandably.

Main Results:

• A novel framework, "demonstrated consent," was developed using blockchain and generative AI.
• Each sample is linked to an NFT that immutably records its research history.
• LLM provides accessible explanations of sample usage, enhancing donor comprehension.

Conclusions:

• Demonstrated consent offers a transparent and verifiable method for tracking biological sample usage.
• Blockchain and AI integration can significantly improve informed consent in research.
• This model empowers donors with clear information about their contributions to scientific research.