Distributed Learning Platform for Interoperable Medical AI Training
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Solution Overview
Problem
The healthcare industry faces challenges in integrating artificial intelligence (AI) due to lack of infrastructure for AI platforms, data fragmentation, interoperability issues, and inefficient techniques for generating annotated training data, which are burdensome and prone to human error.
Innovation Solution
A distributed learning platform that facilitates integrating AI informatics by distributing and connecting hardware and software components for developing and applying AI models, enabling seamless data access, interoperability, and systematic generation of high-quality annotated training data through clinical routines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional centralized AI platforms are used, then data access is restricted to single locations, but this creates data fragmentation and interoperability issues across healthcare organizations
Solution Approach 1:
The system segments the centralized AI platform into distributed learning nodes across multiple healthcare organizations. Each organization maintains local data and model instances, while the federated learning architecture enables collaborative model training without centralizing data. This segmentation resolves the contradiction by enabling interoperability through standardized communication protocols while avoiding the complexity of a single centralized infrastructure.
Solution Approach 2:
The patent implements a nested architecture where local organizational AI systems are embedded within a broader federated learning network. Each healthcare organization hosts local model training and inference capabilities, which are nested within the distributed federated learning platform that coordinates across multiple organizations. This nesting enables interoperability at multiple levels while managing complexity through hierarchical organization.
2Manufacturing precision
If manual techniques are used to generate annotated training data, then data annotation can be performed, but this process is burdensome and prone to human error
Solution Approach 1:
The system implements self-service through automated data annotation using pre-trained AI models that can independently label training data without extensive manual intervention. The federated learning framework enables models to learn from distributed data sources and automatically generate annotations, reducing both the burden on human annotators and the error rate while maintaining high productivity.
Solution Approach 2:
The patent incorporates feedback mechanisms where model predictions are continuously evaluated and used to improve annotation accuracy. Human annotators provide feedback on automated annotations, which is then used to refine the annotation process. This closed-loop feedback system resolves the contradiction by enabling high accuracy through iterative improvement while maintaining high productivity through automation.
3Reliability
If AI models are trained on distributed data across multiple organizations, then data privacy is maintained, but this creates challenges for model coordination and data sharing
Solution Approach 1:
The patent introduces a federated learning server as an intermediary that coordinates model training across distributed organizations without accessing their private data. The server aggregates model updates from participating organizations, manages version control, and coordinates the learning process. This intermediary resolves the contradiction by enabling reliable data privacy through centralized coordination of decentralized learning, making model coordination easy through standardized protocols.
Data Source
AI summary
Techniques are described that facilitate integrating artificial intelligence (AI) informatics in healthcare systems using a distributed learning platform. In one embodiment, a computer-implemented is provided that comprises interfacing, by a system operatively coupled to a processor, with a medical imaging application that provides for viewing medical image data. The method further comprises, facilitating, by the system, generation of structured diagnostic data according to a defined ontology in association with usage of the imaging application to perform a clinical evaluation of the medical image data. The method further comprises providing, by the system, the structured diagnostic data to one or more machine learning systems, wherein based on the providing, the one or more machine learning systems employ the structured diagnostic data as training data to generate or train one or more diagnostic models configured to provide artificial intelligence-based diagnostic evaluations of new medical image data.