AI-Enhanced Precision Psychiatry: Biotech-Driven Mental Health Innovation in Healthcare Systems

Abstract

Mental health disorders continue to pose a critical global health challenge, with traditional psychiatric approaches often limited by subjective assessments and trial-and-error treatments. This study explores the transformative role of AI-enhanced precision psychiatry as a biotech-driven innovation within healthcare systems. Using a mixed-methods design, data were collected from patients diagnosed with depression, anxiety, schizophrenia, and bipolar disorder across multiple clinical sites. Variables included demographic factors, clinical characteristics, biotech-derived biomarkers (genomics, proteomics, metabolomics, neuroimaging), digital biomarkers (speech, facial recognition, smartphone usage, wearable sensor data), and healthcare system outcomes. Advanced AI models including Random Forest, Support Vector Machine, Deep Neural Networks, and LSTM were evaluated for predictive accuracy, interpretability, and clinical applicability. Results indicated that LSTM and deep neural networks achieved the highest predictive performance, while Random Forest offered greater explainability. Integration of biological and digital data improved diagnostic precision, treatment personalization, and healthcare efficiency, with measurable reductions in relapse rates, hospitalizations, and costs. Ethical challenges including data privacy, algorithmic bias, and healthcare inequality were also identified. Overall, the findings underscore the potential of AI-biotech convergence to revolutionize mental health care by shifting toward predictive, personalized, and preventive psychiatry within modern healthcare systems.