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Short abstract:

At home medical kits relying upon biosensors, AI data, and mobile phone app technologies increasingly fill the gaps in publicly provided health care. We propose possible next steps in the participatory design of co-produced medicine based on current AI research in human life extension at Rejuve.bio.

Long abstract:

Recent cuts to public healthcare synchronize with the increasing popularity of at home telehealth systems and the distribution of medical kits, designed and packaged for the easy collection of blood, faeces, and other body samples. The UK-based co-produced medical study zoe.com, for example, represents these broader socio-technical transformations in healthcare through the ‘kit economy’. With the tagline, “Science for every body,” zoe.com markets biosensors, AI data, and mobile phone app technologies to both collect data at the core of their scientific research and to deliver a personalised app product for paying participants.

How might we design future directions for the co-production of medical kits that involve human participants, particularly for life extension studies? This paper assumes a shifting terrain for citizen science: one in which participants are paying to provide data for the development of science and a de-centralised science based on a blockchain model aims to counteract threats of data ownership by private companies. The materiality of the kit provides a welcome tangibility for AI narratives based on data calculations, caught up in media spectacles of the attention economy. Possible kits involve the crowd sourcing of potential therapies, transferring models from animals to humans, testing combinations of materials considered safe, and organoid studies.

Emile Devereaux has thirty years’ experience in digital media and socially-engaged interventions; more recently in participatory design approaches with bio-sensors and maker technologies. In collaboration with Mike Duncan, Computational Biologist and Chief Science Officer at Rejuve.bio, the duo are currently developing responsible AI data frameworks.