Clinical AI Validator & LLM Safety Guardrails
Constructing validation layers to evaluate LLM clinical outputs for safety, accuracy, and compliance.
Project Overview
Generative AI in healthcare offers immense scaling opportunities but poses critical risks from hallucinations and medical inaccuracies. This project focused on building automated, high-precision clinical safety guardrails. We developed real-time prompt verification pipelines that check LLM-generated health content against trusted, peer-reviewed clinical databases before output delivery.
Key Challenges
- 1Mitigating medical hallucination in high-stakes queries (e.g., drug-drug interaction, pediatric dosing).
- 2Balancing model inference speed with multi-step clinical checks.
- 3Aligning LLM voice with compassionate and medically accurate patient communication.
Applied Solutions
- Built a hybrid RAG system linking Google Cloud AI with the proprietary Tata 1mg medicine database.
- Designed 'Reinforcement Learning from Clinical Feedback' (RLCF) loops to fine-tune validation weights.
- Implemented a real-time safety fallback gate triggering clinician review on low-confidence scores.
Core Outcomes
- Successfully audited and secured over 1M monthly medical query responses.
- Achieved a 99.4% validation rate on standard clinical evaluation datasets.
- Recognized by search and health partners as a model framework for Clinical AI governance.
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