A Personalized Patient Preference Predictor for Substituted Judgments in Healthcare: Technically Feasible and Ethically Desirable.

When making substituted judgments for incapacitated patients, surrogates often struggle to guess what the patient would want if they had capacity. Surrogates may also agonize over having the (sole) responsibility of making such a determination. To address such concerns, a Patient Preference Predictor (PPP) has been proposed that would use an algorithm to infer the treatment preferences of individual patients from population-level data about the known preferences of people with similar demographic characteristics. However, critics have suggested that even if such a PPP were more accurate, on average, than human surrogates in identifying patient preferences, the proposed algorithm would nevertheless fail to respect the patient's (former) autonomy since it draws on the 'wrong' kind of data: namely, data that are not specific to the individual patient and which therefore may not reflect their actual values, or their reasons for having the preferences they do. Taking such criticisms on board, we here propose a new approach: the Personalized Patient Preference Predictor (P4). The P4 is based on recent advances in machine learning, which allow technologies including large language models to be more cheaply and efficiently 'fine-tuned' on person-specific data. The P4, unlike the PPP, would be able to infer an individual patient's preferences from material (e.g., prior treatment decisions) that is in fact specific to them. Thus, we argue, in addition to being potentially more accurate at the individual level than the previously proposed PPP, the predictions of a P4 would also more directly reflect each patient's own reasons and values. In this article, we review recent discoveries in artificial intelligence research that suggest a P4 is technically feasible, and argue that, if it is developed and appropriately deployed, it should assuage some of the main autonomy-based concerns of critics of the original PPP. We then consider various objections to our proposal and offer some tentative replies.

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

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.

The Parliamentary Inquiry into Mitochondrial Donation Law Reform (Maeve's Law) Bill 2021 in Australia: A Qualitative Analysis.

Recently, Australia became the second jurisdiction worldwide to legalize the use of mitochondrial donation technology. The Mitochondrial Donation Law Reform (Maeve's Law) Bill 2021 allows individuals with a family history of mitochondrial disease to access assisted reproductive techniques that prevent the inheritance of mitochondrial disease. Using inductive content analysis, we assessed submissions sent to the Senate Committee as part of a programme of scientific inquiry and public consultation that informed drafting of the Bill. These submissions discussed a range of bioethical and legal considerations of central importance to the political debate. Significantly, submissions from those with a first-hand experience of mitochondrial disease, including clinicians and those with a family history of mitochondrial disease, were in strong support of this legislation. Those in support of the Bill commended the two-staged approach and rigorous licencing requirements as part of the Bill's implementation strategy. Submissions which outlined arguments against the legislation either opposed the use of these techniques in general or opposed aspects of the implementation strategy in Australia. These findings offer a window into the ethical arguments and perspectives that matter most to those Australians who took part in the Senate inquiry into mitochondrial donation. The insights garnered from these submissions may be used to help refine policy and guidelines as the field progresses.