Routing Agent Orchestration for Domain-Specific Medical AI Queries
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Solution Overview
Problem
Conventional systems and large language models (LLMs) face challenges in providing accurate and efficient medical information retrieval due to resource intensity, lack of niche data representation, and inaccuracies in evaluating specific medical questions, while oncologists struggle with accessing and analyzing vast health information volumes manually.
Innovation Solution
A platform for generating and deploying task-specific machine-learning agents, allowing users to query medical information through natural language interfaces, with customizable agents configured via a user interface and communicatively coupled to datasets, tools, and output components, enhancing performance and reducing resource and security risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional large language models are used for medical information retrieval, then general medical knowledge coverage is improved, but resource consumption and computational costs increase significantly
Solution Approach 1:
The patent segments the monolithic LLM into multiple specialized agents, each trained on specific medical domains (e.g., oncology, cardiology). This segmentation allows the system to retrieve only relevant domain-specific information rather than activating entire LLM parameters, significantly reducing computational resource consumption while maintaining retrieval accuracy for specific medical queries.
Solution Approach 2:
The patent implements local quality by creating agents with domain-specific expertise tailored to particular medical specialties. Each agent is optimized for its specific domain, concentrating computational resources where they are most needed rather than distributing them uniformly across all medical knowledge areas, thus reducing overall resource consumption while improving local retrieval quality.
2Loss of information
If conventional LLMs are used for medical information retrieval, then broad medical knowledge access is improved, but accuracy in evaluating specific medical questions deteriorates
Solution Approach 1:
The patent divides the general medical knowledge base into specialized domains, with each agent focusing on specific medical questions within its domain. This segmentation enables agents to achieve higher precision in evaluating specific medical questions by concentrating their training and expertise on particular specialties rather than diluting capabilities across all medical topics.
Solution Approach 2:
The patent introduces a routing agent that acts as an intermediary between user queries and domain-specific agents. The routing agent directs specific medical questions to the appropriate specialized agent, ensuring that each question is evaluated by an agent with relevant expertise, thereby improving evaluation accuracy while maintaining broad medical knowledge coverage through the collective capability of multiple agents.
3Loss of information
If oncologists manually analyze vast health information volumes, then comprehensive patient assessment is improved, but time consumption and workload increase significantly
Solution Approach 1:
The patent implements self-service by enabling the multi-agent system to autonomously retrieve, analyze, and synthesize patient data without requiring manual intervention from oncologists. The agents automatically query relevant databases, evaluate patient information against current medical guidelines, and generate treatment recommendations, significantly reducing the time oncologists spend on data analysis while maintaining comprehensive assessment quality.
Solution Approach 2:
The patent introduces AI agents as intermediaries between raw patient data and the oncologist. These agents handle the time-consuming tasks of data retrieval, validation, and initial analysis, presenting processed information and recommendations to the oncologist for final review. This intermediary layer maintains analytical completeness while dramatically reducing the time oncologists must spend on manual data processing.
4Loss of information
If multiple health care institutions share data, then data availability for analysis is improved, but security risks and access control complexity increase
Solution Approach 1:
The patent introduces AI agents as secure intermediaries that mediate access to health data from multiple institutions. These agents implement fine-grained access control policies, authenticating users and authorizing data access on behalf of oncologists. The agents retrieve and process data from multiple sources without requiring direct institutional connections, improving data accessibility while maintaining security through centralized, policy-enforced access control.
Solution Approach 2:
The patent extracts sensitive patient data from secure institutional databases, processes it through AI agents in controlled environments, and returns only necessary results to oncologists. This extraction approach allows data from multiple institutions to be made available for analysis without exposing the underlying databases to external access risks, separating data retrieval from data storage to minimize security vulnerabilities.
Data Source
AI summary
This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components, where each respective task-specific components in the set of task-specific components is configured to assist with a respective task of a plurality of tasks. The method also includes receiving a prompt related to one or more tasks of the plurality of tasks and selecting a subset of task-specific components based on the prompt and the orchestration data. The method further includes coordinating, via a routing agent, interactions between the task-specific components, including providing data related to the prompt to the task-specific components and receiving responses from the task-specific components, and generating a complete response to the prompt that addresses the one or more tasks using the responses from the task-specific components.