A Multicriteria Framework for Evaluation and Selection of Conversational AI Assistants in Mental Health

The rapid proliferation of Conversational Artificial Intelligence Assistants (CAIs) has transformed access to mental health information through freely accessible web interfaces, mobile applications, and public APIs (Application Programming Interfaces), yet systematic methodologies for their evaluation remain limited. This paper introduces SELCAI-MH, a multicriteria framework for CAI evaluation and selection. This framework integrates four complementary multicriteria methods: Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Complex Proportional Assessment Method (COPRAS), and Combinative Distance-based Assessment (CODAS), capturing distance-based, compromise-based, proportional, and negative-ideal logics, and proposes SOLAG, an aggregation method that produces a consensus ranking across methods. SELCAI-MH employs a dual evaluation mechanism combining psychiatric expert assessment with AI-based scoring, expert-derived criterion weights, and domain-relevant conversational datasets. The framework is applied to nine internet-accessible CAIs: proprietary platforms (ChatGPT 5.2, Claude Sonnet 4.5, Gemini 1.5 Flash, Perplexity Sonar, Bing AI/Copilot) and open-source Llama variants deployed via cloud inference endpoints. Using a set of anxiety-related questions and CAI responses, evaluated across seven criteria, Claude Sonnet 4.5 emerged optimal, followed by ChatGPT 5.2 and Gemini 1.5 Flash. SOLAG produced highly consistent rankings across the four multicriteria decision-making (MCDM) methods (Spearman ρ ≥ 0.98). Overall, SELCAI-MH provides a structured and reproducible decision-support framework for selecting accessible CAIs in sensitive mental health contexts.