Algorithm Orchestration for Healthcare Imaging Diagnostics
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Solution Overview
Problem
The healthcare industry faces challenges in effectively integrating and orchestrating artificial intelligence (AI) and machine learning (ML) algorithms for medical imaging diagnostics, despite their potential for improved accuracy and precision, due to complexity in applying these analytics to clinical care.
Innovation Solution
A centralized algorithm orchestration component that executes AI algorithms on medical images according to pre-defined workflows, managing workflow and model execution, and providing notification outputs, including status updates and metadata analysis, to facilitate integration of AI informatics into healthcare systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI algorithms are integrated into healthcare systems for medical imaging diagnostics, then diagnostic accuracy and precision are improved, but system complexity and difficulty in applying analytics to clinical care increase
Solution Approach 1:
The system segments the AI integration process into distinct modular components: an algorithm catalog storing multiple diagnostic algorithms, a workflow definition layer specifying execution sequences, and a workflow execution engine that manages algorithm application. This segmentation reduces overall system complexity by allowing each component to be developed, maintained, and updated independently while working together to achieve accurate diagnostics.
2Adaptability or versatility
If multiple AI algorithms are applied to medical images, then comprehensive diagnostic insights are achieved, but workflow management complexity increases
Solution Approach 1:
Workflows are pre-defined and stored in the system before execution. These workflows specify the sequence of algorithms to apply, their parameters, and execution conditions in advance. When a medical image needs analysis, the system retrieves the appropriate pre-defined workflow from storage and executes it automatically, eliminating the need for real-time workflow management decisions and reducing operational complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where algorithm outputs and diagnostic results are fed back into the workflow execution engine. This enables dynamic adjustment of subsequent algorithm applications based on previous results, ensuring comprehensive diagnostic coverage while automatically managing workflow complexity through result-driven decision making.
3Productivity
If AI algorithms are executed on medical images, then diagnostic efficiency is improved, but data processing and algorithm selection complexity increase
Solution Approach 1:
The workflow execution engine performs self-service by automatically selecting and executing the appropriate algorithms based on the input medical image characteristics and stored workflow definitions. The system autonomously determines which algorithms to apply, manages data processing tasks, and coordinates algorithm execution without requiring manual intervention, thereby improving diagnostic efficiency while containing data processing complexity within the automated engine.
Data Source
AI summary
Techniques for orchestrating execution of algorithms on medical images according to pre-defined workflows and techniques for managing workflow and model execution are provided. In an embodiment, a system comprises a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components comprise an algorithm catalog that comprises algorithm information identifying algorithms available for processing medical images, the algorithm information comprising algorithm execution instructions for executing the algorithms as web-services; and an onboarding component that adds the algorithm information to the algorithm catalog in response to reception of the algorithm information via an onboarding user interface of an algorithm management application, wherein based on inclusion of the algorithm information in the algorithm catalog, the algorithms are made available for incorporating into workflows for executing the algorithms on the medical images.