• Sagar Sharma
• Saransh Aggrawal
• Sakshi Bhatt
• Sheenam Naaz

Due to the reliance on subjective techniques like patient self-reports and therapist assessments, diagnosing mental health problems including PTSD, anxiety, and depression can be challenging. These approaches may result in uneven diagnosis, belated interventions, and restricted access to care, particularly in underprivileged or isolated locations. In order to identify indicators of mental health problems more accurately and effectively, this research suggests an AI-powered diagnostic tool that analyses text, speech, and video. The application uses sophisticated AI models, including as transformers and LSTMs, to identify patterns linked to certain illnesses, giving clinicians a more trustworthy and impartial evaluation. Fairness, openness, and data privacy are highly valued in the tool's design, guaranteeing its moral applicability in practical settings. In order to increase access to mental health treatments, increase the accuracy of diagnoses, and ultimately improve patient care and results, an AI technology supplements the knowledge of mental health professionals. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

Artifical Intelligence Mental Health, Psycholog, Diagnosis

"Beyond the Mind’s Eye: How AI is Unraveling the Mysteries of Mental Health", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.d248-d252, November-2024, Available :http://www.jetir.org/papers/JETIR2411328.pdf

"Beyond the Mind’s Eye: How AI is Unraveling the Mysteries of Mental Health", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppd248-d252, November-2024, Available at : http://www.jetir.org/papers/JETIR2411328.pdf