Algorithmic nudging for clinical trial participation: Autonomy and consent in the era of large language models

Abstract

While large language models (LLMs) hold potential for informing prospective trial participants, their deployment raises critical questions regarding patient autonomy and the integrity of informed consent. This paper examines how LLM-mediated communication may influence trial participation decisions. We situate such influence along a continuum from ethically permissible persuasion to impermissible manipulation and coercion, highlighting the concepts of algorithmic nudging and hypernudging. These advanced techniques, capable of adapting messages in real time based on user-specific data, pose unique risks to voluntariness, especially in sensitive contexts such as clinical research. To explore these dynamics, we performed an explorative case analysis based on trial NCT04387916 (KC1036), a phase I oncology study. We prompted ChatGPT-5 to generate three consent texts: a standard informative summary, a strictly neutral version, and a nudged version employing positive framing and collective benefit appeals. We analyzed them against established ethical categories: voluntariness, risks and discomforts, potential benefits, and scientific and social value. The findings exemplify how even minor linguistic variations can introduce normative assumptions and shift the balance between supporting comprehension and exerting undue influence. We conclude that neutrality in LLM outputs cannot be assumed and persuasion cannot be excluded. Without safeguards that explicitly address the design space, LLMs risk transforming informed consent from a protective mechanism into a mere recruitment tool. Safeguarding autonomy in this context requires careful delineation of ethically acceptable influence, critical awareness of the design space of LLM prompts, and alignment with established research ethics principles to ensure that informed consent remains voluntary, informed, and free from hidden persuasion.