Digital Therapeutic Content Regulation via Risk Model Segmentation
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Solution Overview
Problem
The generation of digital therapeutic content is challenging due to the complexity and personalization required, leading to tedious and resource-intensive content creation. Automated generation using AI techniques can introduce inaccuracies, hallucinations, and lack of relevance, making it difficult to ensure content quality and adherence to specific user conditions.
Innovation Solution
A service that manages an interconnected architecture of multiple risk models across various domains to evaluate content produced by human creators or generative AI. This service determines which content to provide to end users based on risk scores, suggests corrections, and iteratively trains both risk models and generative AI models using feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated AI generation is used to create digital therapeutic content, then productivity and scalability are improved, but content accuracy and relevance deteriorate due to hallucinations and inaccuracies
Solution Approach 1:
The content generation system is divided into multiple independent components: a risk model for safety evaluation, a relevance model for content matching, and a feedback loop for continuous improvement. Each component operates independently and can be optimized separately, allowing the system to maintain high productivity while improving reliability through specialized evaluation modules.
Solution Approach 2:
The system implements feedback mechanisms where user interactions and performance data are continuously fed back into the AI models to refine their output. This feedback loop allows the system to learn from previous errors and improvements, gradually reducing hallucinations and inaccuracies while maintaining automated generation capabilities for high productivity.
2Adaptability or versatility
If multiple AI models are used to generate content for digital therapeutics, then content variety and coverage are improved, but system complexity and computational difficulty increase
Solution Approach 1:
Different AI models are assigned to specific functions: one model generates content variety, another evaluates safety through risk models, and a third ensures relevance to user conditions. This segmentation allows each model to be optimized for its specific task, managing overall system complexity while maintaining high adaptability and content coverage.
Solution Approach 2:
Risk models and relevance models act as intermediary evaluation layers between content generation and delivery. These intermediaries filter and refine the output of multiple AI models, managing complexity by providing a standardized evaluation framework that simplifies the integration of multiple generation models while maintaining diverse content coverage.
3Reliability
If manual content creation is used to ensure accuracy and relevance, then content quality is improved, but resource consumption and time required increase
Solution Approach 1:
The system uses automated AI generation to create the bulk of content (excessive action), then applies selective manual review and risk model evaluation only to critical portions. This partial application of manual processes to key areas maintains content quality while significantly reducing overall time consumption compared to fully manual creation.
Solution Approach 2:
The system performs self-evaluation through integrated risk models and relevance models that automatically assess generated content before delivery. This self-service capability allows the system to maintain high content quality through automated quality control, eliminating the need for extensive manual review and reducing time consumption.
4Loss of energy
If content is provided without risk evaluation, then resource consumption is reduced, but harmful effects and inaccuracies increase
Solution Approach 1:
Risk models perform preliminary evaluation of generated content before it is delivered to users. This preliminary action filters out potentially harmful or inaccurate content early in the process, preventing resource waste on delivering bad content while maintaining efficient resource consumption through automated pre-screening rather than post-error-correction.
Solution Approach 2:
The system uses the computational resources required for risk evaluation to generate beneficial risk scores that improve content quality. By framing the resource consumption as an investment in quality filtering rather than a cost, the system converts what appears to be wasted energy into a beneficial quality control mechanism that prevents harmful content delivery.
Data Source
AI summary
Aspects of the present disclosure are directed to systems, methods, and computer readable media for configuring generation of digital therapeutic content for provision. The system can receive a text input including one or more parameters identifying an audience and at least one domain. The system can identify content items generated by corresponding generative transformer models each using a prompt created based on the text input. The system can select, from a set of risk models for the domains, at least one risk model corresponding to the at least one domain. The system can apply the at least one risk model to each content item of the set of content items to determine a risk score. The system can select a content item based on the risk score of the content item. The system can present the content item including the respective digital therapeutic content.