Generative artificial intelligence has the potential to transform mental health care by enabling personalized, culturally sensitive treatment planning and improving access to services, according to new research from the University of Illinois Urbana-Champaign. Social work professor Cortney VanHook led a study that used generative AI to create a detailed, simulated case study of a fictional client named Marcus Johnson—a young, middle-class Black man in Atlanta experiencing depressive symptoms. The research, published in Frontiers in Health Services, demonstrates how AI can be combined with evidence-based models to address systemic barriers in mental health care. The AI platform generated a comprehensive treatment plan based on the researchers’ prompts, analyzing Marcus’s protective factors—such as family support—and key barriers, including cultural expectations around masculinity, stigma around mental health, and the lack of Black male providers in his insurance network. The simulation was built using three established clinical frameworks: Andersen’s Behavioral Model, which examines personal, cultural, and systemic influences on health care use; a five-component model of access (availability, accessibility, accommodation, affordability, and acceptability); and Measurement-Based Care, which uses standardized tools to track a client’s progress over time. VanHook and his co-authors, Daniel Abusuampeh of the University of Pittsburgh and Jordan Pollard of the University of Cincinnati, ensured the AI-generated content was clinically sound and culturally appropriate. All three are Black men, which helped validate the simulation’s accuracy in representing the unique challenges faced by Black men in the U.S. mental health system. The team also cross-referenced the AI’s recommendations with published research and clinical best practices. The study highlights the value of using AI in a controlled, simulated environment to train students, supervisees, and practitioners. It allows users to explore complex care pathways without violating patient privacy, making it ideal for education and clinical training. VanHook emphasized that the approach is not just theoretical but practical, offering a real-world application of AI in mental health. However, the researchers acknowledge limitations. AI models are only as good as their training data and may not fully capture the emotional depth, cultural nuance, or unpredictability of real clinical interactions. They also note that while the models address many access issues, they cannot fully account for deep-seated structural inequities in health care. Despite these constraints, the team believes generative AI, when used responsibly and in alignment with evidence-based practices, can enhance cultural competence, improve treatment personalization, and expand access to care. VanHook stressed the importance of using AI as a supportive tool, not a replacement for human clinicians. The findings come at a time when Illinois has passed the Wellness and Oversight for Psychological Resources Act, which restricts AI use in mental health to administrative and supplementary support roles under licensed professionals. VanHook clarified that their research model complies with the law, as it is designed for education and clinical supervision, not direct patient care. He urged caution in extending AI use beyond these settings until clearer state guidelines are established.