Radiology Report Impression Generation Using Style-Aware Transformer Models
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Solution Overview
Problem
Current radiology workflows are inefficient and unsatisfactory due to the manual generation of radiology reports, which can vary significantly between radiologists, leading to inconsistent quality and increased time consumption.
Innovation Solution
A system utilizing machine learning models, specifically transformer models, to automatically generate radiology report sections, such as impressions, by receiving radiologist-specific styles and findings, determining context, and inserting impression text that mimics the radiologist's writing style, thereby reducing report generation time and improving consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation of radiology reports is used, then radiologists can generate reports with their own style and judgment, but it requires significant time and effort and leads to inconsistent quality
Solution Approach 1:
The system copies the radiologist's writing style by training on their historical reports and replicating it in generated impressions. This allows automated generation that mimics the radiologist's unique voice and judgment patterns, maintaining consistency across reports while reducing manual effort
Solution Approach 2:
The system changes the parameter of automation level by using machine learning models that can be configured to different degrees of autonomy. The impression generation can range from fully automated to radiologist-reviewed, allowing flexibility in balancing time savings with quality control
2Productivity
If automation of radiology report fields is implemented, then report generation time is reduced, but radiologists are dissatisfied with the content and look of generated fields
Solution Approach 1:
The system copies not just the structure but the actual writing style, tone, and phrasing patterns of the radiologist by training on their historical reports. This creates generated impressions that sound like they were written by the radiologist themselves, significantly improving satisfaction
Solution Approach 2:
The system incorporates feedback loops where radiologist interactions with generated impressions (edits, approvals, rejections) are used to refine and personalize the generation model, continuously improving satisfaction over time
3Extent of automation
If conventional automation attempts are used, then some fields can be automated, but the impression field remains highly dependent on individual radiologists and inconsistent
Solution Approach 1:
The system creates a universal impression generation model that can serve multiple radiologists while adapting to each individual's style. The model processes findings from any modality and generates impressions consistent with the target radiologist's style, making it universally applicable across different practitioners
Data Source
AI summary
A system 100 for automatically generating a field of a radiology report includes a set of one or more models. A method for automatically generating a field of a radiology report includes: receiving a radiologist identifier (radiologist ID); receiving a set of finding inputs; determining a context of each of the set of finding inputs; determining text associated with a portion or all of the radiology report based on the context and the radiologist style; and inserting the text into the report.