Protocol-Specific Federated Learning for Clinical AI Accuracy
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Solution Overview
Problem
Traditional federated learning approaches fail to account for diverse clinical protocols followed by different medical facilities, leading to suboptimal performance and increased false positives/negatives due to the use of a single central AI model not tailored to individual protocols.
Innovation Solution
A system that generates and deploys multiple AI models specific to different clinical protocols, allowing for localized training and updating based on feedback, reducing the need for human intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single central AI model is used for all medical facilities, then device complexity is reduced and ease of operation is improved, but clinical accuracy deteriorates and false positives/negatives increase due to inability to account for diverse clinical protocols
Solution Approach 1:
The patent segments the single central AI model into multiple specialized AI models, each trained on specific clinical protocols. The central server maintains a repository of these segmented models and selectively deploys them based on the clinical protocol of each medical facility, thereby improving clinical accuracy while maintaining manageable system complexity through modular organization.
Solution Approach 2:
The patent implements local quality by training different AI models on specific clinical protocols relevant to different medical facilities. Each AI model is specialized with local knowledge of its designated protocol, ensuring high clinical accuracy for that specific context while the overall system serves multiple facilities with diverse protocols.
2Measurement precision
If multiple AI models specific to different clinical protocols are generated and deployed, then clinical accuracy is improved and false positives/negatives are reduced, but device complexity increases and the system requires more sophisticated model management
Solution Approach 1:
The central server is designed with multi-functionality to handle model training, repository management, selection, and deployment across multiple medical facilities. This universal platform manages the complexity of multiple specialized AI models through a single coordinated system that can adapt to different clinical protocols and facility requirements.
Solution Approach 2:
The system implements dynamics by enabling selective deployment of different AI models based on the clinical protocol of each medical facility. The central server dynamically chooses which specialized model to deploy based on protocol matching, allowing the system to adapt its complexity to the specific needs of each facility rather than maintaining fixed high complexity for all.
3Measurement precision
If AI models are customized for specific clinical protocols, then measurement precision is improved, but the time and resources required for model training and updating increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple AI models on different clinical protocols and storing them in a repository before deployment. The central server has already performed the time-consuming training process in advance, so when a medical facility needs a model, the pre-trained model can be quickly selected and deployed without requiring real-time training, thus reducing the time loss for model acquisition.
Solution Approach 2:
The system implements feedback mechanisms where performance data from deployed AI models is collected and used to guide selective retraining or model updates. Rather than continuously retraining all models, the feedback-driven approach updates only those models that show performance degradation or protocol changes, reducing the overall time and resources required for model maintenance while preserving clinical accuracy.
Data Source
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AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a clinical protocol-based federated learning process. For example, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components that can select, according to a selection criterion applicable to at least one medical facility, a first artificial intelligence (AI) model from a repository comprising a plurality of AI models, deploy the first AI model at the at least one medical facility, access feedback comprising updated parameters of the first AI model generated at the at least one medical facility, and further deploy a second AI model based on the updated parameters and a clinical protocol employed by the at least one medical facility.