Is Consent-GPT valid? Public attitudes to generative AI use in surgical consent

Journal article

Healthcare systems often delegate surgical consent-seeking to members of the treating team other than the surgeon (e.g., junior doctors in the UK and Australia). Yet, little is known about public attitudes toward this practice compared to emerging AI-supported options. This first large-scale empirical study examines how laypeople evaluate the validity and liability risks of using an AI-supported surgical consent system (Consent-GPT).

We randomly assigned 376 UK participants (demographically representative for age, ethnicity, and gender) to evaluate identical transcripts of surgical consent interviews framed as being conducted by either Consent-GPT, a junior doctor, or the treating surgeon.

Participants broadly agreed that AI-supported consent was valid (87.6% agreement), but rated it significantly lower than consent sought solely by human clinicians (treating surgeon: 97.6% agreement; junior doctor: 96.2%). Participants expressed substantially lower satisfaction with AI-supported consent compared to human-only processes (Consent-GPT: 59.5% satisfied; treating surgeon 96.8%; junior doctor: 93.1%), despite identical consent interactions (i.e., the same informational content and display format). Regarding justification to sue the hospital following a complication, participants were slightly more inclined to support legal action in response to AI-supported consent than human-only consent. However, the strongest predictor was proper risk disclosure, not the consent-seeking agent.

As AI integration in healthcare accelerates, these results highlight critical considerations for implementation strategies, suggesting that a hybrid approach to consent delegation that leverages AI’s information sharing capabilities while preserving meaningful human engagement may be more acceptable to patients than an otherwise identical process with relatively less human-to-human interaction.

Authors: Allen, JW; Hannikainen, IR; Savulescu, J; Wilkinson, D; Earp, BD

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: AI and Society
• Acceptance date: 2025-09-17
• EISSN: 1435-5655
• ISSN: 0951-5666
• Language: English
• Keywords: artificial intelligence in healthcare; consent delegation; empirical bioethics. informed consent; large language models; medical ethics.