Modular Dialogue System for Explainable Clinical Interventions
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Solution Overview
Problem
Large language models, despite their advancements in producing human-like text, face challenges in computational efficiency, explainability, and the inability to engage in goal-directed conversations, making them unsuitable for applications like automated therapy without further fine-tuning, which is computationally inefficient and lacks grounded knowledge and therapeutic alliance.
Innovation Solution
A modular dialogue system comprising subject understanding models and a general language model, where the former provides clinical insights and constraints, enabling goal-directed conversations and explainable outputs, while the latter generates human-like responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large language models are used for dialogue systems, then human-like text generation is improved, but computational efficiency deteriorates
Solution Approach 1:
The system divides the dialogue processing into two segments: a subject understanding module that handles clinical analysis and constraint generation, and a language model module that generates human-like responses. This segmentation allows each component to be optimized independently, improving overall computational efficiency while maintaining response quality.
Solution Approach 2:
The patent introduces an intermediary subject understanding module that processes user input and generates constraints before passing information to the language model. This intermediary layer prevents the large language model from processing raw clinical data directly, reducing its computational burden and improving efficiency.
2Reliability
If large language models are used for dialogue systems, then human-like text generation is improved, but explainability deteriorates
Solution Approach 1:
By separating the system into a subject understanding module and a language model module, the patent makes the decision-making process more transparent. The subject understanding module generates explicit constraints and clinical insights that can be independently analyzed and explained, while the language model focuses solely on generating natural-sounding responses based on these constraints.
Solution Approach 2:
The subject understanding module acts as an intermediary that provides explainable clinical reasoning before the language model generates responses. This intermediary layer produces structured outputs including identified disorders, symptoms, and treatment constraints that can be independently verified and explained to users.
3Reliability
If large language models are fine-tuned for specific applications, then performance is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent extracts the application-specific clinical understanding functionality into a separate subject understanding module, removing the need to fine-tune the entire large language model for specific medical applications. This extraction allows the language model to remain a general-purpose, computationally efficient component while the subject understanding module handles application-specific processing.
Solution Approach 2:
The system segments clinical analysis functions from general language generation functions. The subject understanding module handles application-specific clinical processing using smaller, more efficient models, while the language model handles general-purpose natural language generation, avoiding the computational cost of fine-tuning a large model for each application.
4Adaptability or versatility
If large language models are used for dialogue systems, then adaptability is improved, but device complexity deteriorates
Solution Approach 1:
The patent segments the system into specialized modules with clear responsibilities: the subject understanding module handles clinical analysis and adaptation to medical contexts, while the language model handles general language generation. This segmentation reduces overall system complexity by creating modular, independently manageable components.
Solution Approach 2:
The language model serves as a universal component that can handle various dialogue tasks and languages, while the subject understanding module provides application-specific adaptability. This multi-functionality approach allows the system to be adaptable to different medical applications without increasing the complexity of the core language generation capability.
Data Source
AI summary
A dialogue system, comprising: an input configured to obtain input data relating to speech or text provided by a user; an output configured to provide output data relating to speech or text to a user; and one or more processors, the one or more processors being configured to: receive, by way of the input, input data relating to speech or text provided by a user; receive, at a first module, structured information comprising information relating to a clinical state of the user, the structured information being generated from the input data, the first module comprising a subject understanding module and a subject recommendation module, wherein the subject understanding module comprises one or more subject understanding models, each of the one or more subject understanding models configured to take as input the structured information and provide as output subject profile information; generate, at the subject understanding module, subject profile information based on the structured information; determine a subject recommendation comprising an intervention for the user, determining the subject recommendation comprising providing the subject profile information as input to the subject recommendation module; and output, by the way of the output, system responses as a part of a dialogue with the user, the system responses delivering an intervention.