Clinical Decision Support RAG for Context-Specific Diagnosis
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Solution Overview
Problem
Existing large language models (LLMs) in healthcare are deficient due to training on historic or general data, leading to inaccurate recommendations and poor healthcare outcomes, and traditional vector matching struggles with context-specific differential diagnoses.
Innovation Solution
An AI-based clinical decision support platform with a user input processing module, context matching module, retrieval-augmented generation (RAG) module, and output generation module, utilizing physician-validated context libraries and LLMs to generate accurate differential diagnoses, assessments, and treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If LLMs are trained using historic or general data, then training data availability is improved, but recommendation accuracy deteriorates
Solution Approach 1:
The system performs preliminary retrieval of relevant clinical context from the knowledge base before generating recommendations. The RAG module queries the knowledge base using the clinical query and patient data as input, retrieves relevant context, and feeds it to the LLM before final recommendation generation. This preliminary action ensures the LLM has access to current, specific clinical guidelines rather than relying solely on historic training data.
Solution Approach 2:
The retrieval-augmented generation module acts as an intermediary between the LLM and the clinical knowledge base. It translates clinical queries into knowledge base queries, retrieves relevant context, and formats it for the LLM. This intermediary layer allows the system to combine the general reasoning capabilities of the LLM with specific, current clinical knowledge from the knowledge base, resolving the contradiction between using general training data and achieving accurate recommendations.
2Speed
If traditional vector matching is used for context retrieval, then processing speed is improved, but context-specific diagnostic precision deteriorates
Solution Approach 1:
The system changes the retrieval parameter from simple vector similarity to a hybrid approach that incorporates both vector matching and knowledge base schema querying. The RAG module uses the clinical query to search the knowledge base with predefined schemas for differential diagnosis, assessments, and treatment plans. This parameter change allows the system to maintain processing speed while significantly improving the precision of context-specific diagnostic retrieval by leveraging structured clinical knowledge.
Data Source
AI summary
Described are systems and methods for artificial intelligence (AI)-based clinical decision support. Systems can include a platform configured with a user input processing module, a context matching module, a retrieval-augmented generation (RAG) module, and an output generation module. Outputs of the platform can include a differential diagnosis, an assessment and treatment plan, or a clinical reference. The platform can further include an AI-copilot module and an AI-notebook module.