Scan-Based Report Suggestions With Image-Linked AI Feedback
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Solution Overview
Problem
Existing medical reporting methods using machine learning lack interactivity between human practitioners and AI models, limiting the ability to provide effective feedback and standardization in radiological and pathological reporting.
Innovation Solution
A computer-implemented method that integrates a user interface allowing human practitioners to write reports based on scans, with a machine learning model providing real-time suggestions linked to specific image regions, enabling iterative refinement and standardization through a feedback loop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to automatically generate reports from scanned images, then productivity is improved, but reliability deteriorates due to lack of interactivity and feedback between practitioners and AI
Solution Approach 1:
The system implements a feedback mechanism where the ML model generates initial report suggestions, practitioners review and provide corrections, and the model learns from this feedback to improve future report accuracy. This iterative feedback loop resolves the contradiction by maintaining high productivity through automation while improving reliability through continuous learning from practitioner input.
Solution Approach 2:
The practitioner acts as an intermediary between the automated ML system and the final report output. The practitioner reviews ML-generated suggestions, makes necessary corrections based on professional judgment, and approves the final report. This intermediary role ensures that the benefits of automated productivity are maintained while reliability is preserved through human oversight.
2Productivity
If machine learning models autonomously generate reports without practitioner input, then productivity is improved, but loss of information increases due to inability to capture nuanced professional judgement
Solution Approach 1:
The system captures practitioner feedback on ML-generated report suggestions and uses this feedback to refine future predictions. This feedback mechanism preserves professional judgement nuances by continuously learning from practitioner corrections and preferences, preventing information loss while maintaining high generation efficiency.
Solution Approach 2:
The ML model performs preliminary report generation based on scanned images, creating draft reports that capture initial findings. This preliminary action handles routine analysis efficiently, allowing practitioners to focus their expert judgment on reviewing and refining specific aspects, thus preserving nuanced information while maintaining productivity.
3Reliability
If practitioners manually review and write reports from scratch, then reliability is maintained through professional judgement, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The ML model performs preliminary analysis of scanned images and generates draft report suggestions before practitioner review. This preliminary action handles the time-consuming initial analysis and documentation, allowing practitioners to focus their expert judgment on reviewing and refining key findings, thus maintaining reliability while significantly improving productivity.
Solution Approach 2:
The system positions the ML model as an assistant that provides draft report suggestions to practitioners, who then review and refine these suggestions using their professional judgment. This intermediary approach combines the speed of automated generation with the quality assurance of human expertise, resolving the contradiction between reliability and productivity.
4Device complexity
If machine learning models only review report text without analyzing images, then device complexity is reduced, but loss of information increases due to inability to verify findings against actual scan data
Solution Approach 1:
The system segments the analysis process into two parts: the ML model analyzes both images and report text together to generate integrated suggestions, while the practitioner performs final verification. This segmentation allows the model to process visual and textual information jointly without excessive complexity, while preventing information loss through practitioner review of the combined analysis.
Data Source
AI summary
A computer implemented method comprising: receiving a report on a condition of a human or animal subject, composed by a user based on a scan of the subject; inputting the current report and the scan into a trained machine learning model; and based on the report and the scan, the machine learning model generating one or more suggestions for updating the text of the report. The method further comprises causing a user interface to display to the user one or more suggestions for updating the text of the report, with each respective suggestion visually linked in the user interface to a corresponding subregion within at least one image of the scan based upon which the respective suggestion was generated.