A Comparison of AI Mental Health Models and Human Therapy

Abstract

The rapid expansion of artificial intelligence (AI) in healthcare has prompted growing interest in its application to mental health support. This review compares AI-based mental health tools to human psychotherapy from neuroscientific, computational, and clinical perspectives. The review outlines the structure and evidence-based human therapy, with a focus on cognitive behavioral therapy (CBT) and the therapeutic alliance; then, the mechanisms underlying AI mental health models, including large language models, natural language processing, and training techniques such as reinforcement learning from human feedback are explained. A comparison of the human brain and artificial neural networks, and the analysis of empathy plus emotional processing in both systems, is presented. Evidence suggests that while AI tools can temporarily reduce symptoms and improve accessibility to professional help for mild to moderate conditions, they are less effective in cases of severe or complex disorders. Their simulated empathy, which is strongly associated with the success of treatment, differs from natural human emotions and also contributes to the decrease in effectiveness. Key limitations, including privacy concerns, algorithmic bias, and inadequate crisis handling, are also discussed. The review concludes that a hybrid model integrating AI tools with human-delivered care is the most promising direction for the future of mental health treatment.