Medical Report Generation via ML Image Extraction
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Solution Overview
Problem
Manual generation of medical reports during procedures is inefficient, leading to potential information loss and workflow disruptions, as medical professionals must pause to capture and document key moments, increasing procedure time and diverting attention from patient care.
Innovation Solution
A system utilizing machine learning models to automatically identify and extract relevant images and content from medical procedures, generating a draft report that includes auto-generated images, text, and video, allowing for post-procedure selection and updating by the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual image capture and documentation is performed during medical procedures, then medical reports can be generated, but procedure time increases and user focus is diverted from patient care
Solution Approach 1:
The system performs preliminary actions by automatically capturing images and generating report content during the medical procedure without requiring user intervention. The machine learning model continuously analyzes video feeds and extracts relevant frames, preparing the medical report in advance so that minimal or no post-procedure work is needed.
Solution Approach 2:
The system enables self-service by using the medical procedure video feed itself as the source material for report generation. The machine learning model automatically identifies key moments, extracts images, and generates descriptive text without requiring external manual documentation, making the system self-sufficient in creating accurate medical reports.
2Reliability
If manual image capture is performed during medical procedures, then medical reports can be generated, but user focus is diverted from patient care
Solution Approach 1:
The system enables self-service by using the medical procedure video feed itself as the source material for report generation. The machine learning model automatically identifies key moments, extracts images, and generates descriptive text without requiring external manual documentation, making the system self-sufficient in creating accurate medical reports.
Solution Approach 2:
The system replaces the mechanical manual process of image capture and documentation with an automated machine learning-based system. Instead of requiring users to manually select and document images, the ML model automatically performs these tasks, substituting human manual operations with intelligent automated processing.
3Productivity
If postoperative image selection is performed manually, then medical reports can be generated, but crucial information may be forgotten
Solution Approach 1:
The system performs preliminary actions by automatically capturing images and generating report content during the medical procedure without requiring user intervention. The machine learning model continuously analyzes video feeds and extracts relevant frames, preparing the medical report in advance so that minimal or no post-procedure work is needed.
Data Source
AI summary
Described herein are systems, methods, and programming for generating medical reports describing medical procedures. The draft medical report may include auto-generated content describing a medical procedure, where the auto-generated content may include one or more auto-generated images selected based on medical report criteria. The draft medical report may be generated and a user selection of at least one of the auto-generated images may be received. The draft medical report may be updated based on the user selection.