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Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI.

Caspar Barnes1,2, Mateo Riobo Aboy3,4, Timo Minssen5

  • 1Harvard Medical School.

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Summary
This summary is machine-generated.

This study introduces demonstrated consent, using blockchain and AI to track sample use for research. This ensures donors are informed about how their biological materials are utilized, enhancing transparency in research participation.

Keywords:
Informed consentbiomedical researchblockchaingenerative AIlarge language models (LLMs)research ethics

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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.