Image-Based Decision Module for Context-Aware Follow-Up Scans
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Solution Overview
Problem
Existing medical imaging systems require sequential scans based on previous results, leading to time delays, organizational burdens, and patient dissatisfaction due to lack of consideration for clinical context.
Innovation Solution
A decision module and clinical decision system that uses AI/ML to generate decision data based on initial imaging data, clinical findings, and patient position in a clinical guideline, automatically determining the need for and type of follow-up scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential medical imaging scans are performed based on previous results, then diagnostic accuracy is improved, but time delay increases and productivity decreases
Solution Approach 1:
The system performs preliminary analysis of imaging results using AI/ML algorithms to automatically determine the need for follow-up scans and prepare decision data before clinical review, enabling proactive scheduling and reducing waiting time while maintaining diagnostic accuracy
Solution Approach 2:
The system implements automated feedback loops where imaging results are immediately analyzed by AI algorithms, which generate recommendations for subsequent scans. This closed-loop feedback mechanism eliminates manual interpretation delays and ensures rapid progression through the diagnostic sequence
2Reliability
If sequential medical imaging scans are performed based on previous results, then diagnostic accuracy is improved, but organizational burden increases and costs rise
Solution Approach 1:
The system enables self-service automation where the imaging system automatically analyzes its own results, generates follow-up scan protocols, and schedules subsequent examinations without requiring manual organizational intervention. The AI/ML algorithms autonomously manage the diagnostic workflow, reducing administrative complexity while preserving diagnostic rigor
3Productivity
If automated diagnosis is used to reduce time delay, then productivity is improved, but clinical context consideration may be reduced
Solution Approach 1:
The AI/ML-based decision module is designed to perform multiple functions simultaneously: it analyzes imaging data, retrieves relevant clinical information from electronic health records, applies clinical guidelines, and generates comprehensive decision recommendations. This multi-functional capability ensures that automation maintains clinical context awareness while achieving high processing speed
Data Source
AI summary
Techniques are described for providing image-based operational decision support. The technique includes using a decision engine configured to generate output data based on a determined number of clinical findings in medical images of a patient. The decision module is configured to generate decision data depending on position data defining an actual position of the patient in a predefined clinical guideline, which comprises a set of possible next steps subsequent to the actual position. The decision data comprises information indicating which next steps from the set of possible next steps of the clinical guideline should be performed. The disclosure further describes a related method, a clinical decision system, and a related medical imaging system.