Medical Data Summary Interface for Diagnostic Review
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Solution Overview
Problem
The increasing workload and time constraints for medical staff to review medical imaging exams have made it challenging to efficiently analyze and present medical information, despite advancements in artificial intelligence (AI) and machine learning (ML) technologies.
Innovation Solution
A system comprising a processor and memory configured to automatically and dynamically generate summaries of patient information, identify potential medical conditions, and provide potential diagnoses for review, using vectorized content comparison and computer-implemented language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical staff manually review medical imaging exams, then diagnostic accuracy can be maintained, but the time required for review increases significantly
Solution Approach 1:
The patent introduces an AI-based intermediary system that automatically generates structured summaries of medical imaging exams and compares them against a digital library of medical conditions. This intermediary process filters and pre-processes information before human review, maintaining diagnostic accuracy while significantly reducing the time medical staff must spend on manual examination review.
Solution Approach 2:
The system performs preliminary analysis by automatically generating vectored content representations of medical exams and comparing them with the digital library before human review. This preliminary action identifies potential medical conditions and prepares structured summaries in advance, allowing medical staff to focus their expertise on reviewing pre-processed information rather than analyzing raw data from scratch.
2Productivity
If AI and machine learning systems are applied to automate medical imaging tasks, then productivity increases, but the complexity of the system increases
Solution Approach 1:
The AI system is designed to autonomously perform multiple tasks including generating vectored content representations, comparing medical exams against the digital library, identifying potential conditions, and creating structured summaries without human intervention. This self-service capability maximizes productivity while the modular architecture manages complexity by separating distinct functional components.
Solution Approach 2:
The system is divided into distinct functional modules: a vectorization component that converts medical exam data into vectored representations, a comparison engine that matches against the digital library, and a summary generation component. This segmentation manages system complexity by creating independent, manageable components that can be developed and maintained separately while working together to improve overall productivity.
3Loss of information
If comprehensive medical information is presented to medical staff, then complete diagnostic information is available, but the time to review all information increases
Solution Approach 1:
The system extracts and highlights only the most relevant information from comprehensive medical records by comparing vectored content against the digital library of medical conditions. It automatically identifies and presents key findings, potential conditions, and critical data points in structured summaries, allowing medical staff to access complete information when needed while focusing review time on the most diagnostically relevant content.
Solution Approach 2:
The summary generation component applies local quality by providing different levels of information detail in different sections of the output. Critical findings and high-probability conditions receive prominent presentation with detailed information, while less critical data is summarized or omitted, allowing medical staff to quickly grasp essential information while maintaining access to complete data through hyperlinked references.
Data Source
AI summary
Various systems and methods are presented regarding presentation of patient medical data to enable expedited review and/or detailed analysis of the patient medical data. A summary screen can be configured to present a summary of the plethora of available data, wherein the summary screen presents data identified to enable expeditious diagnosis of a patient's condition, while more detailed information is available for presentation on a pop-up screen(s) or navigation to a specific application configured to present information at a greater level of detail/granularity. The summary screen can be configured to present one or more interactive images (e.g., an MRI) in conjunction with text providing further description of the diagnosis, medical data, and suchlike.