Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, Victoria, Australia.
Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, UK.
J Med Ethics. 2024 Jan 23;50(2):77-83. doi: 10.1136/jme-2023-109347.
Obtaining informed consent from patients prior to a medical or surgical procedure is a fundamental part of safe and ethical clinical practice. Currently, it is routine for a significant part of the consent process to be delegated to members of the clinical team not performing the procedure (eg, junior doctors). However, it is common for consent-taking delegates to lack sufficient time and clinical knowledge to adequately promote patient autonomy and informed decision-making. Such problems might be addressed in a number of ways. One possible solution to this clinical dilemma is through the use of conversational artificial intelligence using large language models (LLMs). There is considerable interest in the potential benefits of such models in medicine. For delegated procedural consent, LLM could improve patients' access to the relevant procedural information and therefore enhance informed decision-making.In this paper, we first outline a hypothetical example of delegation of consent to LLMs prior to surgery. We then discuss existing clinical guidelines for consent delegation and some of the ways in which current practice may fail to meet the ethical purposes of informed consent. We outline and discuss the ethical implications of delegating consent to LLMs in medicine concluding that at least in certain clinical situations, the benefits of LLMs potentially far outweigh those of current practices.
在进行医疗或手术程序之前,从患者处获得知情同意是安全和道德临床实践的基本组成部分。目前,将同意过程的很大一部分委托给未进行该程序的临床团队成员(例如,初级医生)是常规做法。然而,同意代表往往缺乏足够的时间和临床知识来充分促进患者的自主权和知情决策。可以通过多种方式解决此类问题。解决这一临床困境的一种可能方法是使用基于大型语言模型的会话人工智能。此类模型在医学中的潜在益处引起了广泛关注。对于委托的程序同意,LLM 可以改善患者获得相关程序信息的途径,从而增强知情决策。在本文中,我们首先概述了在手术前将同意委托给 LLM 的假设示例。然后,我们讨论了现有的同意委托临床指南以及当前实践未能满足知情同意的伦理目的的一些方式。我们概述并讨论了将同意委托给医学中的 LLM 的伦理影响,得出的结论是,至少在某些临床情况下,LLM 的益处可能远远超过当前实践。