AI knows best? Avoiding the traps of paternalism and other pitfalls of AI-based patient preference prediction

Ferrario, Andrea; Gloeckler, Sophie; Biller‐Andorno, Nikola

doi:10.1136/jme-2023-108945

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…In [5,6], the problem of the propensity of machine learneing models and methods to bias in medical data analysis is considered. The research was conducted at the intersection of language models and medical data processing.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Application of a data stratification approach in computer medical monitoring systems

Donets,

Shevchenko,

Holikov

et al. 2024

EEJET

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The research object is the processes occurring in the data stratification subsystem in the medical monitoring computer system, which is part of the decision support system. Such a subsystem aims to solve data analysis and processing problems in the medical monitoring system. Among them, the problems of anomaly detection, data marking, state determination, selection of the most informative variables, and justification of decision-making are selected for solving. The paper proposes the structure and implementation of the data stratification subsystem in the decision support system. The subsystem contains modules for anomaly detection and an autoencoder, a clustering module using an advanced multi-agent clustering method, and a state detection module with a modified neural network training procedure. Modules of the stratification subsystem have been tested using diabetes monitoring data. The results showed that the clustering module provides 25.7 % lower accuracy than the achieved neural network. The accuracy difference is explained by the complexity of the data and the lack of adaptability of the proposed method to solving such problems. It is shown that the method of determining the overall informativeness of variables covers 90 % informativeness with 10 variables, comparable to the variability data. In general, the flexible nature of the proposed stratification subsystem allows for solving the problems. The proposed stratification subsystem offers a robust solution for improving treatment strategies and decision-making in computer medical monitoring systems. Its versatility allows it to be used in any system where decision support is needed, providing valuable information about informative variables and decision-making features for clinicians and researchers

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Application of a data stratification approach in computer medical monitoring systems

Donets,

Shevchenko,

Holikov

et al. 2024

EEJET

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“…According to Jardas, Wasserman, and Wendler ( 2022 ), the first justification would constitute a radical revision of the current practice in many jurisdictions to respect the independent wishes of the patient, while the second could be addressed by incorporating a PPP (or by the same token, a P4) in such a way as to supplement, rather than supplant, human family-based surrogate decision-making (see also Biller-Andorno et al. 2022 ; Ferrario, Gloeckler, and Biller-Andorno 2023b ; however, for a contrary argument that AI-based patient predictors should supplant family as decision-makers if more accurate and less biased, see Hubbard and Greenblum 2020 ).…”

Section: Implementation Privacy and Consentmentioning

confidence: 99%

A Personalized Patient Preference Predictor for Substituted Judgments in Healthcare: Technically Feasible and Ethically Desirable

Earp,

Porsdam Mann,

Allen

et al. 2024

The American Journal of Bioethics

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When making substituted judgments for incapacitated patients, surrogates often struggle to guess what the patient would want if they had capacity. Surrogates may also agonize over having the (sole) responsibility of making such a determination. To address such concerns, a Patient Preference Predictor (PPP) has been proposed that would use an algorithm to infer the treatment preferences of individual patients from population-level data about the known preferences of people with similar demographic characteristics. However, critics have suggested that even if such a PPP were more accurate, on average, than human surrogates in identifying patient preferences, the proposed algorithm would nevertheless fail to respect the patient’s (former) autonomy since it draws on the ‘wrong’ kind of data: namely, data that are not specific to the individual patient and which therefore may not reflect their actual values, or their reasons for having the preferences they do. Taking such criticisms on board, we here propose a new approach: the Personalized Patient Preference Predictor (P4). The P4 is based on recent advances in machine learning, which allow technologies including large language models to be more cheaply and efficiently ‘fine-tuned’ on person-specific data. The P4, unlike the PPP, would be able to infer an individual patient’s preferences from material (e.g., prior treatment decisions) that is in fact specific to them. Thus, we argue, in addition to being potentially more accurate at the individual level than the previously proposed PPP, the predictions of a P4 would also more directly reflect each patient’s own reasons and values. In this article, we review recent discoveries in artificial intelligence research that suggest a P4 is technically feasible, and argue that, if it is developed and appropriately deployed, it should assuage some of the main autonomy-based concerns of critics of the original PPP. We then consider various objections to our proposal and offer some tentative replies.

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Using artificial intelligence to enhance patient autonomy in healthcare decision-making

Guerrero Quiñones

2024

AI & Soc

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AI knows best? Avoiding the traps of paternalism and other pitfalls of AI-based patient preference prediction

Cited by 8 publications

References 8 publications

Application of a data stratification approach in computer medical monitoring systems

Application of a data stratification approach in computer medical monitoring systems

A Personalized Patient Preference Predictor for Substituted Judgments in Healthcare: Technically Feasible and Ethically Desirable

Using artificial intelligence to enhance patient autonomy in healthcare decision-making

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