Medical Institution Analysis Output With AI-Generated Explanations
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Solution Overview
Problem
Analysis results from systems like Japanese Unexamined Patent Application Publication No. 2010-218448 are complex and difficult for non-experts to understand, requiring significant time and expertise.
Innovation Solution
An information processing apparatus and method that utilizes machine learning-based analysis and generation models to acquire and generate output data, including analysis results and explanatory information, making the results easily understandable for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert-level analysis models are used to generate comprehensive analysis results, then the quality and depth of analysis is improved, but the ease of understanding for non-experts deteriorates
Solution Approach 1:
The patent introduces an intermediary component that translates complex analysis results into easily understandable explanations. This mediator layer processes the output from expert-level analysis models and reformulates it in non-technical language, allowing non-experts to comprehend the analysis without losing the depth and quality provided by sophisticated models.
Solution Approach 2:
The system segments the analysis process into distinct components: the analysis model that generates comprehensive results, and a separate explanation generation model that creates understandable interpretations. This segmentation allows each component to specialize - one in analytical depth and the other in communicative clarity - resolving the contradiction between quality and ease of understanding.
2Loss of information
If comprehensive index information is analyzed to generate detailed analysis results, then the information completeness is improved, but the time required to understand the results increases
Solution Approach 1:
The system performs preliminary action by pre-generating explanatory information alongside the analysis results. Instead of requiring users to spend time interpreting complex data themselves, the explanation generation model proactively creates understandable interpretations in advance, reducing the time users need to invest in comprehension while maintaining complete information analysis.
Solution Approach 2:
The explanation generation model acts as an intermediary that bridges the gap between comprehensive analysis results and user comprehension. It processes the complete analysis output and transforms it into accessible explanations, allowing users to quickly understand the full information without being overwhelmed by technical complexity.
3Measurement precision
If sophisticated analysis models are deployed to generate accurate analysis results, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent segments the sophisticated analysis system into modular components: the analysis model, the explanation generation model, and the output generation unit. This segmentation allows the deployment of sophisticated models without overwhelming system complexity, as each module can be independently managed, configured, and optimized. The modular architecture maintains analysis accuracy while making the overall system more manageable.
Solution Approach 2:
The explanation generation model serves multiple functions: it interprets analysis results, generates understandable explanations, and formats output data. This multi-functionality reduces the need for separate specialized components, thereby reducing overall system complexity while maintaining sophisticated analysis capabilities through the core analysis model.
Data Source
AI summary
The information processing apparatus includes at least one memory storing instructions and at least one processor that executes the instructions. The at least one processor executes instructions to acquire index information including a plurality of indexes related to a target medical institution, acquire an analysis result output by an analysis model that that has referred to at least a part of the index information, acquire explanatory information regarding at least a part of the analysis result, the explanatory information being information output from a generation model that has been subjected to machine learning and has referred to at least a part of the analysis result, and generate output data including at least a part of the analysis result and at least a part of the explanatory information.