Danaher and Nyholm ( 2024a ) have recently proposed that digital duplicates—such as fine-tuned, “personalized” large language models that closely mimic a particular individual—might reduce that individual’s _scarcity_ and thus increase the amount of instrumental value they can bring to the world. In this commentary, we introduce the notion of _relational scarcity_ and explore how digital duplicates would affect the value of interpersonal relationships.

Except in extraordinary circumstances, patients' clinical care should reflect their preferences. Incapacitated patients cannot report their preferences. This is a problem. Extant solutions to the problem are inadequate: surrogates are unreliable, and advance directives are uncommon. In response, some authors have suggested developing algorithmic "patient preference predictors" (PPPs) to inform care for incapacitated patients. In a recent paper, Earp et al. propose a new twist on PPPs. Earp et al. suggest we personalize PPPs using modern machine learning (ML) techniques. In (...) this paper, I argue that, contrary to Earp et al.'s claim, personalized patient preference predictors are neither technically feasible nor ethically desirable. (shrink)

Large language models (LLMs) have now entered the realm of medical ethics. In a recent study, Balaset alexamined the performance of GPT-4, a commercially available LLM, assessing its performance in generating responses to diverse medical ethics cases. Their findings reveal that GPT-4 demonstrates an ability to identify and articulate complex medical ethical issues, although its proficiency in encoding the depth of real-world ethical dilemmas remains an avenue for improvement. Investigating the integration of LLMs into medical ethics decision-making appears to be (...) an interesting avenue of research. However, despite the promising trajectory of LLM technology in medicine, it is crucial to exercise caution and refrain from attributing their expertise to medical ethics. Our thesis follows an examination of the nature of expertise and the epistemic limitations that affect LLM technology. As a result, we propose two more fitting applications of LLMs in medical ethics: first, as tools for mining electronic health records or scientific literature, thereby supplementing evidence for resolving medical ethics cases, and second, as educational platforms to foster ethical reflection and critical thinking skills among students and residents. The integration of LLMs in medical ethics, while promising, requires careful consideration of their epistemic limitations. Consequently, a well-considered definition of their role in ethically sensitive decision-making is crucial. (shrink)

Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call “demonstrated consent” that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its metadata (...) information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples. (shrink)

In their recent paper, Earp and coauthors (2024) argue for the ethical desirability of personalized patient preference predictors (P4s): large-language models (LLMs) finetuned on a patient’s “own p...

Recent developments in AI and robotics enable people to create _personalised digital duplicates_ – these are artificial, at least partial, recreations or simulations of real people. The advent of such duplicates enables people to overcome their individual scarcity. But this comes at a cost. There is a common view among ethicists and value theorists suggesting that individual scarcity contributes to or heightens the value of a life or parts of a life. In this paper, we address this topic. We make (...) five main points. First, that there is a plausible _prima facie_ case for the scarcity threat: AI may undermine the value of an individual human life by making us less scarce. Second, notwithstanding this prima facie threat, the role of scarcity in individual value is disputable and always exists in tension with the contrasting view that scarcity is a tragedy that limits our value. Third, there are two distinct forms of scarcity – instrumental and intrinsic – and they contribute to value in different ways. Fourth, digital duplication technology may undermine instrumental scarcity, to at least some extent, but the axiological consequences of this are highly variable. Fifth, digital duplication technology does not affect intrinsic scarcity, and may actually heighten it. (shrink)

Since their public launch, a little over a year ago, large language models (LLMs) have inspired a flurry of analysis about what their implications might be for medical ethics, and for society more broadly. 1 Much of the recent debate has moved beyond categorical evaluations of the permissibility or impermissibility of LLM use in different general contexts (eg, at work or school), to more fine-grained discussions of the criteria that should govern their appropriate use in specific domains or towards certain (...) ends. 2 With each passing week, it seems more and more inevitable that LLMs will be a pervasive feature of many, if not most, of our lives. It would not be possible—and would not be desirable—to prohibit them across the board. We need to learn how to live with LLMs; to identify and mitigate the risks they pose to us, to our fellow creatures, and the... (shrink)

Salloch and Eriksen (2024) present a compelling case for including patients as co-reasoners in medical decision-making involving artificial intelligence (AI). Drawing on O'Neill’s neo-Kantian frame...

Since surrogates are not good at predicting patient preferences, and since these decisions can cause surrogates distress, some have claimed we need an alternative way to make decisions for incapaci...

Ms. P. is in the ICU with respiratory failure and sepsis. She has been on a ventilator for almost a week, and now has impending kidney failure. Her children, who have been taking turns at the bedsi...

New informatics technologies are becoming increasingly important in medical practice. Machine learning (ML) and deep learning (DL) systems enable data analysis and the formulation of medical recomm...

In their target article, “A Personalized Patient Preference Predictor for Substituted Judgments in Healthcare: Technically Feasible and Ethically Desirable,” Earp et al. (2024) discuss ways in whic...

In “A Personalized Patient Preference Predictor for Substituted Judgments in Healthcare: Technically Feasible and Ethically Desirable,” Earp et al. (2024) offer what should be considered a potentia...

In this reply to our commentators, we respond to ethical concerns raised about the potential use (or misuse) of personalized LLMs for academic idea and prose generation, including questions about c...

Background Artificial intelligence (AI) has revolutionized various healthcare domains, where AI algorithms sometimes even outperform human specialists. However, the field of clinical ethics has remained largely untouched by AI advances. This study explores the attitudes of anesthesiologists and internists towards the use of AI-driven preference prediction tools to support ethical decision-making for incapacitated patients. Methods A questionnaire was developed and pretested among medical students. The questionnaire was distributed to 200 German anesthesiologists and 200 German internists, thereby focusing on physicians who (...) often encounter patients lacking decision-making capacity. The questionnaire covered attitudes toward AI-driven preference prediction, availability and utilization of Clinical Ethics Support Services (CESS), and experiences with ethically challenging situations. Descriptive statistics and bivariate analysis was performed. Qualitative responses were analyzed using content analysis in a mixed inductive-deductive approach. Results Participants were predominantly male (69.3%), with ages ranging from 27 to 77. Most worked in nonacademic hospitals (82%). Physicians generally showed hesitance toward AI-driven preference prediction, citing concerns about the loss of individuality and humanity, lack of explicability in AI results, and doubts about AI’s ability to encompass the ethical deliberation process. In contrast, physicians had a more positive opinion of CESS. Availability of CESS varied, with 81.8% of participants reporting access. Among those without access, 91.8% expressed a desire for CESS. Physicians' reluctance toward AI-driven preference prediction aligns with concerns about transparency, individuality, and human-machine interaction. While AI could enhance the accuracy of predictions and reduce surrogate burden, concerns about potential biases, de-humanisation, and lack of explicability persist. Conclusions German physicians frequently encountering incapacitated patients exhibit hesitance toward AI-driven preference prediction but hold a higher esteem for CESS. Addressing concerns about individuality, explicability, and human-machine roles may facilitate the acceptance of AI in clinical ethics. Further research into patient and surrogate perspectives is needed to ensure AI aligns with patient preferences and values in complex medical decisions. (shrink)

A Patient Preference Predictor (PPP)—an algorithm capable of predicting, on the basis of demographic or more personalized data, what an incapacitated patient would prefer were they capacitated—is a...

In the age where AI is showing increasing potential to solve problems in unprecedented ways, it becomes tempting to see it as the solution for every problem, resulting in a focus on the means (i.e....

In “A Personalized Patient Preference Predictor for Substituted Judgments in Healthcare,” Earp et al. (2024) introduce the Personalized Patient Preference Predictor (P4), an AI model designed to ex...

The debate about the chances and dangers of a patient preference predictor (PPP) has been lively ever since Annette Rid and David Wendler proposed this fascinating idea ten years ago. Given the tec...

Any human or AI system ambitious enough to mine my every social media post, purchase, or Internet search history is likely to infer some very accurate details about me. I am in my 30s, an avid trav...

The concept of a Patient Preference Predictor—an algorithm that supplements or replaces the process of surrogate decision-making for incapacitated patients—was first suggested a decade ago (Rid and...

Making decisions for incapacitated patients has been a perennial problem in bioethics. Surrogate decision-makers are sometimes expected to use substituted judgment to make such decisions. Applying...

The future of medicine will be predictive, preventive, personalized, and participatory. Recent technological advancements bolster the realization of this vision, particularly through innovations in...

Earp et al. (2024) sketch a thought-provoking potential use of generative AI to enhance supported decision-making for adults who have lost capacity/competence to make their own medical decisions. T...