Guest Editors
Assoc. Prof. Weike Zhang
Email: zhangwk@scu.edu.cn
Affiliation: School of Public Administration, Sichuan University, Chengdu, 610065, China
Homepage:
Research Interests: AI-Driven Mental Health Coverage Policies: Developing & evaluating public and private insurance reimbursement frameworks for AI-based mental health diagnostics and teletherapy.
Cost-Effectiveness & Equity: Assessing the cost-benefit, affordability, and equitable access of AI mental health tools within social health insurance systems.
Regulatory Governance: Formulating policy and regulatory strategies for integrating AI into mental healthcare, focusing on data privacy, liability, and ethical insurance practices.
Vulnerable Populations: Designing AI-supported, insurance-financed interventions targeting mental health needs of underserved groups (e.g., persons with disabilities, elderly).
Implementation Challenges: Analyzing systemic barriers and facilitators for adopting AI in mental healthcare under existing medical insurance schemes.
Dr. Ming Zeng
Email: zengming@mail.xhu.edu.cn
Affiliation: School of Economics, Xihua University, Chengdu, 610039, China
Homepage:
Research Interests: AI-Powered Mental Health Cost Optimization: Analyzing how AI can enhance efficiency and reduce costs in mental healthcare delivery, potentially improving medical insurance fund sustainability.
Insurance Design for AI Mental Health Tools: Evaluating the integration and reimbursement mechanisms for AI-driven diagnostics/therapies within public and private medical insurance schemes.
Equity & Access: Assessing the impact of AI-enabled mental health services covered by insurance on reducing disparities in access and outcomes across demographic groups.
Behavioral Insights for Adoption: Investigating psychological factors influencing the uptake of AI mental health services by patients and providers within insured systems.
Policy Simulations: Modeling the long-term fiscal and societal impacts of integrating AI into mental healthcare under different medical insurance policy frameworks.
Summary
1. Background & Importance
Global mental health systems face triple challenges: ineffective clinical outcomes (e.g., diagnostic delays), fragmented insurance coverage for innovative treatments, and limited public health reach. Intelligent solutions leveraging AI—from predictive analytics to virtual therapists—offer unprecedented opportunities to transform this landscape. Yet, their potential remains constrained by disjointed reimbursement models, ethical risks, and scalability gaps. This SI addresses the critical nexus of AI innovation, equitable healthcare, and mental health.
2. Aim & Scope
This SI aims to advance research on AI-driven mental health solutions that synergistically optimize:
· Clinical outcomes (accuracy, personalization, therapeutic efficacy)
· Insurance pathways (reimbursement design, cost control, sustainable financing)
· Public health impact (accessibility, equity, population wellness)
We seek interdisciplinary studies bridging technology, health economics, and policy.
3. Suggested Themes
· Clinical validation of AI diagnostics/digital therapeutics
· Personalized treatment algorithms and outcome measurement
· Insurance reimbursement frameworks for AI mental health services
· Medical Insurance System Reform(Financing models, payment mechanisms e.g., DRG/value-based, pooling levels)
· Cost-effectiveness and sustainability of intelligent solutions
· Equity-focused deployment (rural/underserved communities)
· Public health integration (preventive care, stigma reduction)
· Policy governance (data privacy, liability, cross-sector collaboration)
· Other relevant but not limited to the above-mentioned themes
Keywords
artificial iIntelligence, mental health, digital health, healthcare systems, medical insurance reform, cinical outcomes optimization, health equity, health policy
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