Distributed ML Layers for Real-Time Multimodal Clinical Insights
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Solution Overview
Problem
Current medical systems lack the ability to effectively process and derive insights from vast amounts of multimodal data, leading to subpar outcomes, increased spending, and inefficiencies in procedural medicine, particularly in complex procedures like TAVR, due to antiquated data management and lack of unified data tools for clinical decision-making and AI capabilities.
Innovation Solution
A system and methods integrating distributed machine learning layers for real-time processing of multimodal datasets, including images, scans, and videos, to provide an AI-powered platform that enhances medical procedures by delivering real-time insights, optimizing billing, and streamlining clinical coordination, while ensuring privacy-preserving data management and regulatory compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current medical systems use antiquated data management approaches, then system simplicity is maintained, but the ability to process and derive insights from multimodal data deteriorates
Solution Approach 1:
The system segments data processing into specialized modules: a data collection module for gathering multimodal data, a distributed machine learning module for processing and analyzing data across multiple layers, and an AI model generation module for creating predictive models. This segmentation allows complex data processing to be divided into manageable, independent components that can be optimized separately.
Solution Approach 2:
The patent introduces distributed machine learning layers as intermediary components between raw multimodal data and clinical decision-making. These intermediary layers process and transform data through multiple processing stages, enabling insights to be derived without requiring direct complex analysis of all raw data, thus reducing the burden on the overall system.
2Quantity of substance
If health systems collect and store vast amounts of data, then data availability for analysis improves, but the ability to derive actionable insights deteriorates due to lack of unified data tools
Solution Approach 1:
The system merges multiple data types (imaging data, scan data, video data, and other multimodal data) into a unified structure that can be processed together. The distributed machine learning architecture combines these diverse data sources across multiple processing layers, enabling the system to derive actionable insights from the combined data volume that would be impossible from individual data types alone.
Solution Approach 2:
The patent transitions from two-dimensional data storage to a multi-dimensional processing architecture using distributed machine learning layers. This dimensional change enables the system to process data not just as flat collections, but as multi-layered structures with temporal, spatial, and contextual dimensions, allowing deeper insights to be extracted from the same data volume.
3Speed
If real-time processing of multimodal data is implemented, then decision-making speed improves, but computational resource requirements worsen
Solution Approach 1:
The computational workload is segmented across multiple distributed machine learning layers, with each layer processing specific aspects of the data. This segmentation allows the system to distribute computational burden rather than concentrating all processing in a single resource-intensive operation, enabling real-time processing while managing energy consumption through parallel processing.
Solution Approach 2:
The system applies partial processing at each distributed layer, where not all data requires full-depth analysis at every stage. This partial action approach allows the system to achieve real-time processing by applying computational intensity only where necessary, reducing overall energy consumption while maintaining processing speed through optimized selective computation.
Data Source
AI summary
An integrated medical platform with system and methods for enhancing medical procedures by rapid-data integration, artificial intelligence (“AI”) analyses with real-time, data-driven insights generated by machine learning models, which are executed in real time and continuously evolving to assist medical professionals in providing favorable outcomes. Other aspects of the integrated medical platform are configured to improve quality of procedures, automate regulatory headaches and streamline clinical coordination to improve outcomes and cost. In some embodiments, a unified tracking system is configured to track TAVR procedures introduces hospitals to an integrated, multimodal AI-enabled platform designed as an all-in-one platform configured to improve planning and care coordination of complex procedures. Smart data collection optimizes billing and streamlines registry data capture. It provides predictive clinical guidance powered by privacy-preserving federated deep learning and generative AI improves patient care.