A multi-criteria approach for evaluating the Use of AI for Matching Patients to Optimal Mental Health Treatment Plans

ABSTRACT

This study presents a novel approach to assessing different artificial intelligence alternatives for planning mental health treatments. Using a fuzzy TOPSIS-based method, six AI alternatives, including rule-based systems, logistic regression, neural networks, evolutionary algorithms, hybrid models, and benchmark algorithms were evaluated. The assessment was based on several criteria: privacy protection, treatment effectiveness, explainability, healthcare costs, regulatory compliance, and ethical implications. Rule-based systems and benchmark algorithms emerged as the most preferred techniques. The study underscores the importance of considering various criteria and viewpoints from stakeholders when creating AI-driven decision-support systems for mental health treatment. The main areas of future research should be the development of ethical and explainable AI, validation studies, integrating AI with emerging technologies, and encouraging stakeholder involvement. The results of this study provide a foundation for informed decision-making and guide further investigation and advancement in this domain, with the aim of revolutionizing the provision of mental health services and improving patient outcomes.

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WSN 193(2) (2024) 201-222

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