Medical Imaging Workflow Support via LLM Inference Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Clinical information is fragmented and often unstructured, leading to a loss of vital data, increased workload, and delayed or disrupted patient care due to the complexity of medical imaging workflows, which are hindered by data silos, vendor-specific solutions, and the ambiguity of natural language in medical documentation, resulting in inefficient workflow automation and increased risk of errors.
Innovation Solution
A computer-implemented method using a large language model to transform unstructured clinical information into structured format through an inference graph, enabling rule-driven derivation of medical imaging workflows, allowing clinicians and technologists to adjust protocols via natural language interactions, and providing intelligent support through an AI-based assistance function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual processing of unstructured clinical reports is used, then data completeness is maintained through human interpretation, but workload increases and processing time is extended
Solution Approach 1:
The patent replaces manual mechanical processing of clinical reports with an AI-based natural language processing system. The AI assistant automatically extracts structured information from unstructured reports, transforming the mechanical task of data extraction from human clinicians to an automated computational system, thereby maintaining data completeness while significantly improving workflow efficiency
Solution Approach 2:
The AI assistant enables self-service by automatically performing data extraction and structuring tasks without requiring manual intervention. The system autonomously processes clinical reports, generates structured data, and integrates it into the imaging workflow, allowing the system to serve itself rather than requiring continuous human operational input
2Stability of the object's composition
If standardized vocabularies and protocols are implemented across diverse clinical IT systems, then data consistency is improved, but implementation difficulty increases due to data silos and vendor-specific solutions
Solution Approach 1:
The AI assistant acts as an intermediary layer between diverse clinical IT systems and standardized vocabularies. Instead of directly integrating multiple vendor-specific systems, the AI processes unstructured data from various sources and transforms it into standardized structured formats, mediating the complexity of inter-system integration while ensuring data consistency
Solution Approach 2:
The patent segments the complex task of cross-system data integration into smaller, manageable components. The AI assistant handles specific extraction tasks for different data types (clinical indications, patient history, findings) separately, processing each segment independently and then integrating them into a unified structured output, thereby reducing overall implementation complexity
3Adaptability or versatility
If scan protocols are manually configured and adapted for each clinic, then protocol specificity to local requirements is achieved, but time and expertise requirements increase significantly
Solution Approach 1:
The AI assistant performs preliminary action by automatically generating draft scan protocols based on extracted clinical information before human review. The system pre-configures protocol parameters, selects appropriate imaging sequences, and prepares protocol recommendations in advance, allowing clinicians to review and adjust rather than create protocols from scratch, thereby reducing configuration time while maintaining adaptability
Solution Approach 2:
The system implements feedback mechanisms where AI-generated protocol recommendations are reviewed and adjusted by clinicians, and this feedback is used to refine future protocol generation. The system learns from manual adjustments and feedback to improve its protocol adaptation accuracy over time, maintaining high adaptability while reducing the time required for configuration
4Measurement precision
If advanced image acquisition techniques are used, then diagnostic capability is enhanced, but cognitive burden on clinicians increases and focus on patient care is reduced
Solution Approach 1:
The AI assistant extracts and processes complex information from clinical reports and imaging data, separating the cognitive burden of data interpretation from clinicians. The system automatically extracts relevant clinical indications, patient history, and imaging findings, presenting only the essential information and recommendations to clinicians, thereby enhancing diagnostic capability through advanced techniques while reducing cognitive burden
Data Source
Figure 1~3
Figure 4~5
AI summary
In one aspect the invention relates to a computer-implemented method for providing medical imaging workflow support data, the method comprising: - receiving (S1) natural language data (U), the natural language data (U) comprising clinical information, - generating (S2) structured information (N) by applying a large language model (L) to the natural language data (U), the structured information (N) comprising the clinical information in a structured format of at least one input node (31, 32) of an inference graph (G), - calculating (S3) the medical imaging workflow support data by applying at least one rule of the inference graph (G) to the structured information (N), and - providing (S4) the medical imaging workflow support data.