Decision Support Framework for Quality Assurance and Enhancement of Therapeutic Artificial Intelligence Systems: Mixed Methods Pilot Study.

Background: Therapeutic chatbots are increasingly deployed across digital mental health services, yet most evaluation efforts remain diagnostic rather than actionable. Organizations lack structured pathways to translate evaluation findings into validated quality improvements aligned with health care quality assurance requirements. Objective: This study aims to introduce EvaluationPlus, a decision support framework that operationalizes a reproducible evaluation-to-enhancement loop for therapeutic artificial intelligence systems. We aimed to demonstrate its feasibility through expert-guided diagnosis, multi-large language model (LLM) enhancement mapping, and within-subject validation. Methods: Using the bilingual mental health chatbot Dr. CareSam (GPT 4.0-based), we conducted 3 iterative enhancement cycles. Two licensed clinical psychologists performed structured diagnostic reviews using think-aloud protocols to identify competency-specific deficits across a 7-dimension therapeutic competency rubric. Three LLMs (GPT 4.0, Claude 4.0 Sonnet, Gemini 2.5 Flash) generated prescriptive enhancement strategies aligned with identified gaps. A participant-blinded, within-subject A/B validation study with Korean graduate students from the Department of Applied Artificial Intelligence, Sungkyunkwan University (N=15; 16 recruited, 1 excluded; IRB-approved) compared baseline and enhanced versions across standardized clinical scenarios spanning mild anxiety, to crisis-level presentations. Results: The enhanced system demonstrated substantial improvement in overall therapeutic quality, with mean scores increasing from 5.40 to 7.63 (Δ =+2.23 points, 41%; dz=0.881; 95% bootstrap CI [0.32-2.20]). Prespecified target dimensions - active listening and appropriate questions, personalization, and complex thinking - showed large-effect improvements (mean gain +3.04; dz range 0.96-1.08), significantly exceeding gains in nontargeted dimensions (+1.62; targeting differential +1.42 points). Directional improvement was observed in 13 of 15 participants (86.7%). User preference strongly favored the enhanced system (13/15, 86.7%), and expert clinical evaluation confirmed maintained safety and therapeutic appropriateness across four scenario severity levels (preference rate 75%; 3 of 4 scenarios). Cross-participant rating consistency improved substantially (coefficient of variation: 20% → 8.1%). Conclusions: EvaluationPlus demonstrates feasibility as a structured framework for iterative quality assurance of therapeutic artificial intelligence systems. By linking expert diagnostic procedures with prescriptive multi-LLM enhancement mapping and multistakeholder validation, the framework supports reproducible improvement cycles relevant to organizational oversight of digital mental health tools. Limitations include a small pilot sample, single-culture focus, and simulated crisis scenarios; future work should extend validation to diverse clinical populations and longitudinal outcome assessment.