PsycheGen: A GenAI-Based Framework for Multimodel Mental Health Profiling and Personalized Intervention

Most traditional mental health psychological risk prediction depends on self-reports and interview questions designed by professionals that do not really measure a patient’s real-time experience or make each patient’s experience unique. PsycheGen presents a Generative AI −based ecosystem that encompasses multimodal data (speech, text, facial expressions, physiological signals) to develop a composite of an individual patient’s mental health profile and to develop individualized mental health interventions. By utilizing deep learning integrating generative models, PsycheGen is able to decode behavioral changes as well as emotional signatures enabling accurate and real-time streaming analysis of well-being. This approach enables PsycheGen to effectively deliver adaptive personalized recommendations through a digital interlocutor as well as virtual companions, like Diana and Henry avatars that allow patients to relate and share their feelings and experiences. Through the enabling of AI-driven diagnostics and generative therapeutic responses, PsycheGen is fundamentally improving accessibility, precision, personalization, and the level of mental healthcare delivery. Emerging pilot studies have revealed excellent optimal findings: average mood recognition accuracy of 89%, F1-score for psychological risk prediction is 0.84, and a 27% personalization improvement over baseline conversational systems. Summing up the user experience Khairu scores from 94/100 to 114/100, response latency below 1.5 seconds, where seconds can assure real-time engagement and feasibility.