Should Artificial Intelligence-Based Patient Preference Predictors Be Used for Incapacitated Patients? A Scoping Review of Reasons to Facilitate Medico-Legal Considerations.

Abstract

Background: Research 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.

Objectives: This scoping review aims to provide a thorough analysis of the arguments, both for and against their use, presented in the academic literature.

Methods: A 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.

Results: The 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.

Conclusions: Further 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.