Medical Report Generation via Human-Algorithm Feedback Loop
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Solution Overview
Problem
Current medical report generation methods lack reproducibility, consistency, and time efficiency, often resulting in non-uniform reports due to human interpretation variability, which can lead to misinterpretation of medical conditions and suboptimal treatment.
Innovation Solution
A method and system that involves user input to generate final findings, which are used to automatically create reports and train algorithms for pattern recognition, improving reproducibility and consistency by allowing human validation and feedback for machine findings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If medical reports are generated manually by medical personnel, then the reports can be customized and reflect individual expertise, but the reports become non-uniform and lack reproducibility
Solution Approach 1:
The system incorporates feedback mechanisms where medical personnel validate and correct algorithm-generated report sections. This feedback loop allows the system to learn from corrections and improve its report generation consistency while maintaining the expertise input of medical professionals, thereby achieving uniformity without complete automation complexity
Solution Approach 2:
The report generation process is divided into segments where different parts of the report can be generated by different methods - algorithmic generation for standardized sections and manual input for specialized findings. This segmentation allows the system to achieve uniformity in standardized sections while preserving the flexibility needed for complex medical interpretations
2Productivity
If manual report generation is used, then medical personnel can incorporate their expertise and judgment, but the process is time-consuming and less efficient
Solution Approach 1:
The system performs self-service by automatically generating report sections based on processed medical data, reducing the time medical personnel need to spend on routine reporting. The algorithm processes data and drafts reports independently, with medical personnel only needing to review and correct specific sections, thereby increasing productivity while maintaining reliability through human oversight
3Manufacturing precision
If different medical personnel interpret the same data, then diverse perspectives can be brought to the analysis, but consistency and reproducibility of findings deteriorate
Solution Approach 1:
The system standardizes the parameters and criteria used for interpreting medical data by using algorithmic processing with consistent thresholds and evaluation metrics. This parameter standardization ensures that the same data is interpreted consistently across different cases and by different users, improving finding consistency while the system maintains operational flexibility through configurable parameters that can be adjusted based on specific medical contexts
Data Source
AI summary
The present invention relates to a method for generating a report (50) comprising receiving a user input to thereby generate at least one final finding (24); automatically generating the report (50) based on the at least one final finding (24); and utilizing the at least one final finding (24) to train at least one algorithm (12), wherein the at least one algorithm (12) is configured to generate at least one machine finding (22). The present invention also relates to a corresponding system and use.