Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Nov 16, 2025
Open Peer Review Period: Nov 26, 2025 - Jan 21, 2026
Date Accepted: Jun 15, 2026
(closed for review but you can still tweet)
From Evaluation to Enhancement: A Decision Support Framework for Quality Assurance in Therapeutic AI Systems
ABSTRACT
Background:
Therapeutic chatbots are increasingly deployed across digital mental health services, yet most evaluation efforts remain diagnostic rather than actionable. As a result, organizations lack structured pathways to translate evaluation findings into validated quality improvements suitable for clinical governance.
Objective:
This study introduces EvaluationPlus, a decision support framework that operationalizes a reproducible evaluation→enhancement loop for therapeutic AI systems. We aimed to demonstrate its feasibility through expert-guided diagnosis, multi-LLM enhancement mapping, and within-subject validation.
Methods:
Using the bilingual mental health chatbot Dr.CareSam, we conducted three iterative enhancement cycles. Two clinical psychologists performed structured diagnostic reviews using think-aloud protocols to identify competency-specific deficits. Three large language models (GPT-4.0, Claude 4.0 Sonnet, Gemini 2.5) generated prescriptive enhancement strategies aligned with a seven-dimension therapeutic competency rubric. A double-blind, within-subject A/B study with Korean university students (N=15; IRB-approved) compared baseline and enhanced versions across standardized scenarios.
Results:
Enhancement cycles yielded substantial overall improvement in therapeutic quality (+21%; 6.26→7.59; dz=0.71). Targeted dimensions (Active Listening and Questioning, Personalization, Complex Thinking) improved by +3.04 points, exceeding gains observed in non-targeted domains (+1.62). User preference strongly favored the enhanced system (87%), and expert ratings confirmed maintained safety and therapeutic appropriateness.
Conclusions:
EvaluationPlus provides a governance-ready framework for continuous quality assurance of therapeutic AI systems. By linking expert diagnostic procedures with prescriptive LLM-driven enhancements and multi-stakeholder validation, the framework supports reproducible improvement cycles suitable for clinical deployment. Clinical Trial: Not applicable. This study was a non-clinical performance-evaluation experiment, not a clinical trial.
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