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Beyond algorithmic trust: interpersonal aspects on consent delegation to LLMs
  1. Zeineb Sassi1,
  2. Michael Hahn2,
  3. Sascha Eickmann1,
  4. Anne Herrmann-Johns1,3,
  5. Max Tretter4
  1. 1 Epidemiology and Preventive Medicine, Medical Sociology, University of Regensburg, Regensburg, Germany
  2. 2 Theology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
  3. 3 School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
  4. 4 Theology, FAU, Erlangen, Germany
  1. Correspondence to Zeineb Sassi, Epidemiology and Preventive Medicine, Medical Sociology, University of Regensburg, Regensburg, Germany; zeineb.sassi{at}klinik.uni-regensburg.de

https://doi.org/10.1136/jme-2023-109799

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    Consent-GPT: is it ethical to delegate procedural consent to conversational AI?

    In their article ‘Consent-GPT: is it ethical to delegate procedural consent to conversational AI?’, Allen et al 1 explore the ethical complexities involved in handing over parts of the process of obtaining medical consent to conversational Artificial Intelligence (AI) systems, that is, AI-driven large language models (LLMs) trained to interact with patients to inform them about upcoming medical procedures and assist in the process of obtaining informed consent.1 They focus specifically on challenges related to accuracy (4–5), trust (5), privacy (5), click-through consent (5) and responsibility (5–6), alongside some pragmatic considerations (6). While the authors competently navigate these critical issues and present several key perspectives, we posit that their discussion on trust in what they refer to as ‘Consent-GPT’ significantly underestimates one vital factor: the interpersonal aspect of trust.

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    Footnotes

    • Funding This study was funded by Bundesministerium für Bildung und Forschung (01GP2202A).

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; internally peer reviewed.

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