Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering
Project Overview
The document presents 'Illuminate', an innovative AI-driven mobile application that utilizes generative AI to improve the detection and treatment of depression in educational settings. By harnessing advanced Large Language Models (LLMs) like GPT-4, Llama 2, and Gemini, the application aims to deliver precise diagnoses and empathetic interactions while offering personalized therapy grounded in established cognitive-behavioral therapy (CBT) techniques. Key applications include providing support to students facing mental health challenges, thereby addressing critical issues such as stigma and accessibility that often hinder traditional mental health support systems. The design of 'Illuminate' prioritizes explainability and transparency, ensuring that users can engage meaningfully with the technology. Findings from the implementation of the application suggest that it could significantly enhance mental health support for students, promoting better educational outcomes and overall well-being. The outcomes indicate a promising shift in how generative AI can be effectively integrated into educational environments, creating a more responsive and supportive framework for mental health management.
Key Applications
Illuminate mobile application for depression detection and therapy
Context: Mental health support for individuals with depression, utilizing insights from social media and clinical interviews.
Implementation: Integrates LLMs with prompt engineering methodologies for diagnosis, interaction, and personalized therapy.
Outcomes: Reported improvements in diagnostic accuracy, user engagement, and provision of empathetic support.
Challenges: Stigma surrounding mental health, potential misdiagnosis, and the need for user trust in AI-driven recommendations.
Implementation Barriers
Social
Stigma associated with mental health conditions that may deter individuals from seeking help.
Proposed Solutions: Increasing awareness and education about mental health, promoting the use of AI tools as supportive rather than replacement therapies.
Technical
Challenges in ensuring the accuracy, explainability, and reliability of AI-driven diagnoses.
Proposed Solutions: Utilizing rigorous validation techniques, including cross-validation and user feedback, to enhance model reliability.
User Trust
Users may be skeptical about the reliability of AI-generated mental health assessments.
Proposed Solutions: Building transparency in AI processes and providing clear explanations for AI recommendations to foster trust.
Project Team
Aryan Agrawal
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Aryan Agrawal
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai