ReviewHealthcare (Basel, Switzerland)2025
Should Artificial Intelligence-Based Patient Preference Predictors Be Used for Incapacitated Patients? A Scoping Review of Reasons to Facilitate Medico-Legal Considerations.
Review in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- The PALLI-AI Framework: An Evidence-Informed Checklist for Designing and Reporting Trustworthy Artificial Intelligence Tools in Palliative and End-of-Life Care.Healthcare (Basel, Switzerland) · 2026Article
- Knowledge, perception, and attitude of healthcare students towards artificial intelligence: a multi-center cross sectional study.BMC medical education · 2026Article
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- Patient preference predictors revisited: technically feasible, ethically desirable, yet must be clinically relevant.Critical care (London, England) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundResearch indicates that surrogate decision-makers often struggle to accurately interpret and reflect the preferences of incapacitated patients they represent. This discrepancy raises important concerns about the reliability of such practice. Artificial intelligence (AI)-based Patient Preference Predictors (PPPs) are emerging tools proposed to guide healthcare decisions for patients who lack decision-making capacity.
objectivesThis scoping review aims to provide a thorough analysis of the arguments, both for and against their use, presented in the academic literature.
methodsA search was conducted in PubMed, Web of Science, and Scopus to identify relevant publications. After screening titles and abstracts based on predefined inclusion and exclusion criteria, 16 publications were selected for full-text analysis.
resultsThe arguments in favor are fewer in number compared to those against. Proponents of AI-PPPs highlight their potential to improve the accuracy of predictions regarding patients' preferences, reduce the emotional burden on surrogates and family members, and optimize healthcare resource allocation. Conversely, critics point to risks including reinforcing existing biases in medical data, undermining patient autonomy, raising critical concerns about privacy, data security, and explainability, and contributing to the depersonalization of decision-making processes.
conclusionsFurther empirical studies are needed to assess the acceptability and feasibility of these tools among key stakeholders, such as patients, surrogates, and clinicians. Moreover, robust interdisciplinary research is needed to explore the legal and medico-legal implications associated with their implementation, ensuring that these tools align with ethical principles and support patient-centered and equitable healthcare practices.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.