AI Response Evaluation via Domain-Specific and Agnostic Guidance
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Solution Overview
Problem
There is a need to evaluate the quality, accuracy, and safety of responses generated by AI large language models like ChatGPT for patient-related questions in radiation oncology, as existing technologies lack comprehensive methods for assessing these aspects.
Innovation Solution
A method is introduced that combines domain-specific and domain-agnostic evaluations to assess the quality of ChatGPT-generated responses. This involves generating responses to patient queries, performing domain-specific evaluations using expert guidance, and domain-agnostic evaluations through statistical analysis, to provide a comprehensive assessment of response accuracy, completeness, and potential harm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI large language models are used to generate responses for patient questions, then efficiency and accessibility are improved, but reliability and accuracy deteriorate due to hallucinations and factually inaccurate responses
Solution Approach 1:
The patent introduces an intermediary evaluation system that acts as a mediator between the AI model and the final response. This system includes domain-specific evaluators (medical experts, fact-checking systems) and domain-agnostic evaluators (readability, bias detection) that verify and validate AI-generated responses before they reach patients, thus resolving the contradiction between efficiency and reliability
Solution Approach 2:
The patent implements a feedback mechanism where AI-generated responses are evaluated by multiple evaluators and the results are used to improve future responses. The system continuously learns from evaluation feedback, adjusting its response generation process to reduce hallucinations and improve accuracy while maintaining efficiency
2Reliability
If comprehensive evaluation methods are implemented for AI responses, then reliability is improved, but device complexity and evaluation time increase
Solution Approach 1:
The patent segments the evaluation system into distinct modular components: domain-specific evaluators (medical accuracy, fact-checking) and domain-agnostic evaluators (readability, bias, sensitivity). Each module performs a specific evaluation function independently, making the complex evaluation process manageable, maintainable, and scalable while ensuring comprehensive response quality assessment
3Measurement precision
If domain-specific evaluation using expert guidance is used, then measurement precision is improved, but loss of time increases due to manual review processes
Solution Approach 1:
The patent performs preliminary automated evaluations using domain-specific algorithms and fact-checking systems before human expert review. This preliminary action filters out obviously incorrect responses and prepares evaluation materials in advance, allowing human experts to focus only on complex cases that require their specialized judgment, thus reducing overall evaluation time while maintaining high precision
Data Source
AI summary
A method for evaluating an artificial intelligence (AI) large language model (LLM) generated response, the method comprising receiving a user query in the form of patient-related questions for a medical treatment domain; analyzing the user query using a LLM learned with open-source data and outputting a LLM answer from the LLM; performing a domain-specific evaluation of the LLM answer; performing a domain-agnostic evaluation of the LLM answer; generating at least one metric for the LLM answer based on the domain-specific evaluation and the domain-agnostic evaluation of the LLM answer; and evaluating the quality of the LLM answer based on the at least one metric.