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BACKGROUND The integration of artificial intelligence (AI) in mental health care has traditionally been hampered by the reliance on static models that necessitate extensive training and fine-tuning, thereby limiting their adaptability and personalization capabilities 12. OBJECTIVE This study aims to introduce an innovative framework that utilizes the linguistic capabilities of large language models (LLMs), specifically GPT-3.5, to create a dynamic, user-specific language and conversational corpus that aligns with the individual psychological and linguistic profiles of patients. METHODS Our approach, termed 'spontaneous language symbiosis and combustion,' eliminates the need for conventional training or fine-tuning. It establishes a symbiotic relationship between the LLM and the user's unique conversational data, facilitating real-time adaptation to the user's evolving language and mental state. RESULTS Implementation of this methodology has shown significant advancements in personalizing patient interactions, incorporating temporal abstractions, and enhancing the AI's operational framework to effectively anticipate and address adverse behavioral patterns. Ethical considerations were meticulously evaluated to ensure a balanced deployment that maximizes benefits while minimizing dependency risks 34. CONCLUSIONS The findings from this study suggest that this cutting-edge AI-driven tool offers a cost-effective, adaptable, and empathetic alternative to traditional mental health care methodologies. By democratizing AI in healthcare, our tool emerges as a promising universal solution for mental health support, poised to transform clinical practices and mark a significant milestone in the convergence of artificial intelligence and healthcare. CLINICALTRIAL
Jonas Colin (Tue,) studied this question.