Universal AI Integration Service for Medical Image Quality Control
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Solution Overview
Problem
Current medical imaging systems lack quality control workflows for AI-generated results, as they are limited by the DICOM standard, which restricts the ability to refine AI models and provide feedback, leading to invalidated results and lost opportunities for model tuning.
Innovation Solution
A universal AI integration service (UAIS) is introduced to facilitate a workflow repository and engine between medical image storage devices and AI orchestration platforms, enabling stateless AI model execution, persistent storage of results, and end-user feedback mechanisms, allowing for quality control and refinement of AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are executed against medical images in a DICOM environment, then clinical findings can be generated, but quality control workflows for AI-generated results cannot be implemented
Solution Approach 1:
The patent introduces a feedback service as an intermediary component between the AI orchestration platform and the DICOM environment. This feedback service receives AI-generated results, stores them in a results store, and manages feedback loops that allow quality control without modifying the core DICOM infrastructure. The intermediary handles the complexity of feedback workflows, enabling reliability improvements while maintaining compatibility with existing systems.
Solution Approach 2:
The system segments the AI workflow into distinct functional components: the AI orchestration platform executes models, the feedback service manages quality control and feedback loops, and the results store persists AI-generated results. This segmentation allows each component to specialize in specific tasks, with the feedback service specifically handling quality control and model refinement feedback, thereby resolving the contradiction between reliability and adaptability.
2Adaptability or versatility
If the DICOM standard is strictly followed, then system compatibility is maintained, but the ability to refine AI models and provide feedback is restricted
Solution Approach 1:
The feedback service acts as a mediator that bridges the gap between the simple DICOM standard and the complex requirements of AI model refinement. It implements sophisticated feedback loops, result persistence, and quality control workflows while presenting a simplified interface to the rest of the system. This intermediary absorbs the complexity, allowing AI model refinement capabilities without increasing overall system complexity.
Solution Approach 2:
The patent implements explicit feedback mechanisms where AI-generated results are stored and can be reviewed, evaluated, and used to refine models. The feedback service captures feedback from clinicians and other users, stores it systematically, and uses it to improve AI model performance over time. This feedback loop enables adaptability for model refinement while the feedback service manages the complexity of collecting, storing, and processing feedback information.
3Loss of information
If AI results are generated and stored, then clinical utility is improved, but invalidated results are not removed leading to data quality issues
Solution Approach 1:
The feedback service implements continuous monitoring and feedback mechanisms to track the validity of stored AI results. When results are invalidated (e.g., due to source image rejection or model updates), the feedback service detects this through feedback loops and automatically removes or marks the invalidated results. This feedback-driven approach ensures both retention of valid results and removal of invalidated ones, resolving the contradiction between information retention and data quality.
Solution Approach 2:
The patent replaces manual verification and cleanup of AI results with an automated feedback-driven system. Instead of relying on manual processes to identify and remove invalidated results, the feedback service automatically monitors result validity, detects invalidations through feedback loops, and manages the removal or marking of invalid results. This substitution of automated mechanics ensures data quality while preserving valid results.
Data Source
AI summary
Systems and methods are provided for integrating artificial intelligence (AI) workflows. In one example, a method includes receiving, from a medical image storage device, an instance availability notification, in response to determining that the notification indicates that one or more medical images have been saved at the medical image storage device, querying the medical image storage device to retrieve metadata associated with the one or more medical images, initiating a work-item with an AI orchestration platform for the metadata, and receiving, from the AI orchestration platform via the work-item, AI results related to the one or more medical images.