AI-Driven Mental Healthcare 5.0: A Survey of Opportunities and Challenges in Leveraging Large Language Models and Generative AI

Abstract

The integration of Large Language Models (LLMs) and Generative AI into mental healthcare is reshaping diagnostic, therapeutic, and support systems, marking a transition toward AI-driven mental healthcare. This paper explores various methodologies for incorporating LLMs into mental health applications, categorizing research into key strategies such as prompt engineering, fine-tuning, emotional theory integration, AI-powered chatbots, and long-term memory architectures. Through case studies, we evaluate their effectiveness in areas like depression detection, suicide risk assessment, and AI-assisted psychological counseling, highlighting both their potential benefits and associated challenges. While LLMs enable more empathetic interactions, personalized interventions, and early risk identification, concerns remain regarding bias, misinformation, safety risks, and ethical issues related to privacy, informed consent, and AI’s role in clinical decision-making. We outline critical research gaps, stressing the importance of real-world validation, standardized evaluation methods, and strong risk mitigation frameworks. This paper serves as a guide for the responsible, scalable, and ethical integration of AI in mental healthcare, ensuring these technologies enhance patient well-being while adhering to clinical and ethical standards.

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