P4s Are Either Unhelpful or Unnecessary. Proposing a Better AI-Powered Solution to Predict Patients' Preferences

The Personalized Patient Preference Predictor (P4) has been proposed as an AI tool to aid surrogate decision-making when incapacitated patients lack advance directives. Unlike population-level Patient Preference Predictors (PPPs), which infer preferences from demographic correlations, P4s fine-tune large language models (LLMs) on a patient's digital footprint to simulate their likely treatment preferences. The goal is to preserve autonomy by grounding predictions in individualized data rather than broad statistical trends. This paper argues that P4s face a fundamental dilemma: they are either unnecessary or unhelpful. When relevant, individualized evidence of preferences exists in a patient's digital footprint, the P4 is unnecessary and suboptimal, since the original data can be examined directly, with richer context than a generative model can preserve. When no such evidence exists, the P4 is unhelpful or misleading, producing plausible-sounding outputs that in the best case rely on population-level correlations rather than the patient's own values. To address these limitations, I propose a better AI-powered alternative: the Patient Preference Retriever (PPR). Rather than generating new text, the PPR uses vector search techniques to retrieve relevant statements from a patient's digital footprint, presenting them verbatim alongside metadata such as date, context, and source. This approach offers greater transparency, respects autonomy more reliably, and supports surrogate decision-makers in weighing authentic evidence. I conclude that while advance directives remain the gold standard, retrieval-based approaches like the PPR provide a more reliable and ethically defensible use of AI in surrogate decision-making than generative approaches like P4s.