Orchestrator Engine for Context-Driven Radiology AI Workflow
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Solution Overview
Problem
The integration of AI in radiology is hindered by data bias, unstructured radiology data, and the lack of contextual understanding, leading to reduced accuracy and inefficiency in radiology workflows, as well as the separation of radiologists from clinician insights due to outdated PACS systems.
Innovation Solution
A computer program and system utilizing an orchestrator engine to provide a context-driven workflow for radiology images and patient data, selecting the appropriate AI program for processing, and structuring results for radiologists, which includes data normalization and orchestration to ensure accurate and efficient AI outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If PACS systems are used to manage radiology workload, then radiologist productivity increases, but radiologists are separated from clinician insights and contextual understanding
Solution Approach 1:
The patent introduces an orchestrator engine as an intermediary component that sits between PACS and radiologists. This orchestrator retrieves contextual clinician insights from electronic health records and other sources, then integrates them with radiology images before presenting to radiologists. This mediator enables both high productivity through automation and preservation of clinician insights through structured data integration.
2Productivity
If AI programs are deployed to process radiology data, then productivity increases, but data bias and lack of contextual understanding reduce accuracy
Solution Approach 1:
The patent implements preliminary action by having the orchestrator engine prepare and normalize radiology data before it reaches AI programs. The orchestrator retrieves relevant patient context, clinical notes, and demographic information in advance, structures this data according to AI requirements, and pre-processes images with metadata. This preliminary preparation ensures AI receives comprehensive, contextualized data that reduces bias and improves diagnostic accuracy while maintaining high processing efficiency.
3Reliability
If radiology data is stored in proprietary PACS formats, then data storage is maintained, but data normalization and sharing for AI training becomes difficult
Solution Approach 1:
The patent applies parameter changes by implementing a data normalization layer in the orchestrator engine that transforms proprietary PACS formats into standardized structures suitable for AI training. The orchestrator maintains the original stored data integrity while creating normalized versions with consistent metadata, tagging, and formatting. This parameter transformation enables seamless data sharing across different PACS systems and prepares data for multi-center AI training without compromising storage reliability.
4Adaptability or versatility
If multiple AI programs are used to process different radiology data types, then processing versatility increases, but data orchestration complexity increases
Solution Approach 1:
The patent implements universality by designing the orchestrator engine as a multi-functional platform that handles diverse radiology data types (images, reports, structured data) and routes them to appropriate AI programs through a unified interface. The orchestrator provides universal data normalization, quality control, and workflow management capabilities that work across all AI applications. This universal approach enables processing versatility while managing orchestration complexity through standardized protocols and centralized control logic.
Data Source
AI summary
Provided are a computer program product, system, and method for an orchestrator engine to provide context driven workflow of radiology images and patient data to artificial intelligence engines for further processing. An orchestrator engine, providing context driven workflow, processes the patient information to determine an artificial intelligence program of a plurality of artificial intelligence programs to process the medical image. The artificial intelligence program processes the medical image and the patient data to provide a structured result as output. The structured result with the patient information is forwarded to the radiologist to evaluate the medical image.