Multimodal Medical Report Generation From ECG and Test Data
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Solution Overview
Problem
The generation of medical reports is time-consuming, prone to human error, and lacks standardization due to individual variations in training and subjective judgment among medical practitioners, especially when integrating heterogeneous data types from medical tests like echocardiograms and electrocardiograms.
Innovation Solution
An apparatus and method utilizing a multimodal large language model (LLM) to generate medical reports by processing electrocardiographic signals, down-sampling in the time dimension, mapping embeddings into a latent space, and concatenating with user queries to create a comprehensive data structure for report generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation of medical reports is used, then medical practitioners can exercise professional judgment and adapt to individual cases, but the process is time-consuming and produces inconsistencies due to individual variations in training and experience
Solution Approach 1:
The patent uses large language models to copy and replicate the reporting style and structure of experienced medical practitioners. The model is trained on standardized report templates and examples, allowing it to generate consistent reports that follow established conventions without the variability introduced by different practitioners' training and experience levels.
Solution Approach 2:
The system changes the fundamental parameter of report generation from human-based to AI-based. By using large language models that process medical data through standardized computational algorithms rather than human judgment, the system achieves both speed and consistency while maintaining medical accuracy through model training on validated datasets.
2Adaptability or versatility
If heterogeneous data types from multiple medical tests are integrated, then comprehensive diagnostic information can be synthesized, but the complexity of integrating and standardizing different data formats increases
Solution Approach 1:
The patent employs a universal data processing framework where a single large language model can handle multiple types of medical data (textual reports, numerical values, imaging data) through unified processing mechanisms. The model uses embedding layers and projection layers that can accommodate different data modalities, converting them all into a common representation space for consistent processing and report generation.
Solution Approach 2:
The system introduces intermediary processing layers including embedding layers and projection layers that act as mediators between different data types and the final report generation. These intermediary components standardize and normalize heterogeneous data formats, transforming them into a unified representation that simplifies integration while preserving the richness of the original multi-modal data.
3Measurement precision
If specialized cardiologists and imaging modalities are used, then high accuracy in diagnostic interpretation can be achieved, but access and cost are limited
Solution Approach 1:
The patent implements a self-service diagnostic system where the large language model autonomously processes medical data and generates diagnostic reports without requiring specialized cardiologists. The model performs self-learning from training data and can independently interpret various medical test types, providing accurate diagnostics while increasing accessibility to patients who previously could only access specialized care through limited channels.
Data Source
AI summary
An apparatus for the generation of a medical report is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a user query. The memory instructs the processor to receive a user profile comprising a plurality of medical tests. The memory instructs the processor to generate testing data as a function of the plurality of medical tests using an encoder. The memory instructs the processor to generate textual data that is representative of the testing data using a querying transformer model (Q-former). The memory instructs the processor to generate a medical report as a function of the user query and the textual data using a report large language model (LLM).