Enterprise AI Analysis
AI-Powered Psychiatric Diagnosis: Revolutionizing Mental Health Assessments
This paper introduces a novel Fine-Tuned Large Language Model (LLM) Consortium and OpenAI-gpt-oss Reasoning LLM-enabled Decision Support System. It addresses the inherent subjectivity and variability in traditional psychiatric diagnoses by leveraging fine-tuned LLMs trained on conversational datasets to predict mental disorders with high accuracy. The system aggregates individual model predictions through a consensus-based process, refined by a dedicated reasoning LLM. This end-to-end architecture, facilitated by LLM agents, aims to standardize psychiatric diagnoses, enhance clinical reliability, and ensure responsible AI in mental healthcare.
Quantifiable Impact of AI in Healthcare
Implementing AI for psychiatric diagnosis offers significant improvements in accuracy, efficiency, and consistency, transforming clinical workflows and patient outcomes.
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Improved Diagnostic Consistency
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Reduction in Compute Cost (QLoRA)
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Faster Diagnostic Workflow
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Enhanced Scalability for Deployment
Deep Analysis & Enterprise Applications
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Enhanced Model Performance Through Fine-tuning
The system leverages a consortium of fine-tuned Large Language Models (LLMs) like Llama-3, Mistral, and Qwen2. These models are adapted using techniques like QLoRA to specialize in psychiatric diagnostic conversations, achieving high accuracy even on consumer-grade hardware.
39.69%
Peak Memory Utilization with QLoRA for Training
| Feature | Before Fine-tuning | After Fine-tuning |
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| Diagnosis Output |
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| Reasoning Quality |
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| Diagnostic Accuracy |
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Orchestrating Intelligence: LLM Agents and Reasoning
A dedicated LLM Agent Layer orchestrates the entire diagnostic workflow, from generating prompts to aggregating results. The OpenAI-gpt-oss Reasoning LLM then synthesizes these diverse inputs to deliver a final, robust diagnosis, ensuring interpretability and reliability.
Enterprise Process Flow
LLM Agent: Prompt Generation (Data Lake)
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Fine-Tuned LLM Consortium: Preliminary Diagnostics
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LLM Agent: Aggregate & Format
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OpenAI-gpt-oss: Synthesize & Refine
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Final DSM-5 Aligned Diagnosis
OpenAI-gpt-oss: Consensus-Driven Diagnostic Refinement
In a case involving Post-Traumatic Stress Disorder (PTSD), Llama-3 and Mistral models both predicted PTSD with DSM-5 codes, while Qwen-2 reported 'Unknown'. The OpenAI-gpt-oss reasoning LLM meticulously analyzed the patient's symptoms (flashbacks, avoidance behaviors) against DSM-5 criteria, considering the agreement of two models and the caution of the third. It then provided a detailed chain-of-thought, confirming PTSD as the most clinically accurate diagnosis despite Qwen-2's uncertainty, based on the presence of necessary symptoms. This demonstrates the model's ability to reconcile diverse inputs, apply structured clinical logic, and deliver a robust, evidence-based final diagnosis, significantly enhancing reliability.
Advancing Clinical Practice and Responsible AI
The proposed system is designed to provide clinicians with a robust, evidence-based, and transparent tool, improving diagnostic precision and consistency. Its architecture emphasizes responsible AI principles, paving the way for next-generation eHealth systems.
95%
Improved Diagnostic Consistency & Reliability
| Platform | Domain | Fine-tuning Support | Running LLM | Vision LM Support | Reasoning LLM Support | LLM Consortium Support |
|---|---|---|---|---|---|---|
| Psychiatric-Diagnoses | Psychiatric | ✓ | Llama-3, Mistral, Qwen-2 | X | ✓ | ✓ |
| Med-PaLM [56] | General medicine | ✓ | PaLM | ✓ | X | X |
| LLM for DDx [57] | General medicine | ✓ | Not specified | X | X | X |
| Me-LLaMA [37] | General medicine | ✓ | Llama | X | X | X |
| CDSS [12] | Mental Health | X | GPT-4 | X | ✓ | X |
| DrHouse [45] | General medicine | ✓ | Not specified | ✓ | ✓ | X |
| Weda-GPT [34] | Indigenous Medicine | ✓ | Llama-3 | X | X | X |
Projected ROI for Your Enterprise
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Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A clear path to integrating AI for standardized psychiatric diagnoses within your organization, from pilot to full deployment.
Phase 1: Discovery & Pilot (2-4 Weeks)
Initial assessment of existing diagnostic workflows, data infrastructure, and specific clinical needs. Set up a secure data lake with anonymized conversational data. Conduct a pilot with a small consortium of fine-tuned LLMs on a specific mental health condition (e.g., depression) to validate diagnostic accuracy and consistency.
Phase 2: Customization & Integration (4-8 Weeks)
Fine-tune additional LLMs (Mistral, Qwen2) on broader psychiatric datasets. Develop custom LLM Agents for seamless orchestration and prompt engineering. Integrate the OpenAI-gpt-oss Reasoning LLM for robust consensus-driven diagnoses. Establish secure API endpoints and initial clinician feedback loops.
Phase 3: Validation & Scalability (6-12 Weeks)
Extensive clinical validation with diverse patient cases and multiple psychiatrists. Optimize models for deployment on consumer-grade hardware using QLoRA. Implement robust monitoring for model performance, bias, and adherence to DSM-5 criteria. Prepare for multi-site deployment and expand diagnostic capabilities.
Phase 4: Full Deployment & Continuous Improvement (Ongoing)
Roll out the full decision support system across target clinical environments. Establish continuous learning pipelines for model updates and adaptation to new clinical guidelines. Integrate multimodal inputs (voice, facial expressions) and multilingual support. Monitor long-term impact on diagnostic standardization and patient outcomes.
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