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TRUVACE RECORD VERSION
record: TRV-2026-1403
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-15T06:53:45.190668Z
status: published
lens: p_space
sector: health
headline: Beyond Autonomy: Ethical Considerations of AI in Substitute Decision-Making for Incapacitated Patients
dek: The integration of artificial intelligence (AI) into healthcare offers significant potential for enhancing patient care, particularly for incapacitated patients unable to communicate their preferences. Advanced AI systems, such as Personalized Patient Preference Predictors (P4), are increasingly proposed as tools to assist in substitute decision-making, yet their ethical implications remain underexamined. This paper critically examines the ethical implications of employing AI-driven preference prediction systems…
gain_title: (none)
problem_title: Personalized Patient Preference Predictors may infer probabilities but cannot capture the relational, contextual and value-laden nature of real patient preferences, leaving risks of algorithmic paternalism and latent bias.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Personalized Patient Preference Predictors may infer probabilities but cannot capture the relational, contextual and value-laden nature of real patient preferences, leaving risks of algorithmic paternalism and latent bias.
problem_evidence: they cannot fully capture the relational, contextual, and value-laden nature of authentic patient preferences | Algorithmic paternalism and latent bias remain salient risks, even in highly personalized models
quick_read: A paper in the Journal of Evaluation in Clinical Practice examines AI systems called Personalized Patient Preference Predictors, which are proposed to help decide for patients who cannot communicate their wishes. It tests them against four ethical principles: autonomy, beneficence, non-maleficence and justice.

The analysis concludes that these tools may augment preference prediction but should not replace the judgment of human surrogates. The significance is that clinical teams may be tempted to treat a prediction as the patient's voice. The source presents this as an ethical analysis, so how the tools perform in real clinical settings remains untested here.
limitation: The work is a conceptual ethical analysis based on principled reasoning, and the supplied text reports no empirical testing of P4 systems in clinical practice, so real-world performance is unmeasured.
tag: Evidence-backed problem
key_points: The paper evaluates P4 systems against autonomy, beneficence, non-maleficence and justice. | It distinguishes probabilistic preference inference from genuine autonomy. | Bias can persist even in highly individualized datasets. | AI is framed as a decision-support tool that augments human surrogates rather than replacing them. | Recommendations include meaningful human oversight, transparent and interpretable AI, and a robust regulatory framework.
rundown: The paper starts from the premise that AI could improve care for incapacitated patients who cannot state their preferences. Systems such as P4 are increasingly proposed for substitute decision-making, but their ethical implications are described as underexamined.

Using principled ethical reasoning, the authors identify three main challenges: the gap between predicted preferences and genuine autonomy, algorithmic paternalism even in personalized models, and subtle bias within highly individualized datasets.

The authors propose a framework that places AI predictions within a model of relational autonomy, with human surrogates contextualizing the AI-generated data. Their recommendations are meaningful human oversight, transparency and interpretability, and a regulatory framework.
sources:
- peer_reviewed | Journal of Evaluation in Clinical Practice | https://doi.org/10.1111/jep.70661 | 2026-10-01
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