AI Tumor Management with Federated Clinical Learning
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Solution Overview
Problem
Existing tumor management systems lack context awareness due to inadequate training of AI systems outside the clinical environment, requiring excessive time from human experts and missing real-world data points, leading to inefficiencies in providing timely value to treatment teams.
Innovation Solution
An AI-ATMS that enables real-time engagement with multidisciplinary teams, allowing on-site training through federated databases and participant feedback to enhance context awareness, utilizing an artificial intelligence component to provide context-related data elements during clinical meetings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If AI systems are trained outside the clinical environment, then training time from human experts is reduced, but context awareness and real-world data accuracy deteriorate
Solution Approach 1:
The AI system trains itself by automatically consuming real-world clinical data from electronic health records, medical imaging systems, and treatment databases. The system performs self-supervised learning where it identifies patterns and relationships in clinical data without requiring manual annotation or intervention from healthcare experts, thereby eliminating the need for experts to leave their clinical duties while maintaining high context awareness.
Solution Approach 2:
The system introduces an intermediary layer that connects AI training with clinical data sources. This intermediary architecture allows the AI to access and learn from real clinical data through standardized interfaces and APIs, enabling training within the clinical environment without directly involving human experts in the training process. The intermediary layer ensures data privacy and security while providing the AI system with access to authentic clinical contexts.
2Ease of manufacture
If subject matter experts are removed from clinical duties to train AI systems, then AI training can be performed, but clinical productivity and patient care quality deteriorate
Solution Approach 1:
The AI system performs self-training by automatically processing and learning from clinical data sources including electronic health records, medical imaging, laboratory results, and treatment outcomes. The system uses self-supervised learning techniques to identify meaningful patterns and relationships in the data without requiring human experts to manually annotate or curate training data, thus eliminating the need to remove experts from their clinical responsibilities.
Solution Approach 2:
The system performs preliminary data processing and preparation automatically, pre-processing clinical data into suitable formats for AI training before the actual model training begins. This preliminary action includes data cleaning, normalization, feature extraction, and annotation using automated algorithms, which prepares the data for training without requiring human expert intervention, thereby preserving clinical productivity while enabling comprehensive AI training.
3Quantity of substance
If AI systems are trained without real-world clinical data, then training resource requirements are reduced, but context awareness and treatment relevance deteriorate
Solution Approach 1:
The system introduces intermediary components that serve as bridges between the AI training process and real-world clinical data sources. These intermediaries include data access layers, privacy-preserving computation modules, and standardized communication interfaces that enable the AI system to consume authentic clinical data from electronic health records, medical imaging systems, and treatment databases while maintaining data security and patient privacy. This intermediary architecture allows the AI to learn from real-world contexts without direct exposure to sensitive patient information.
Solution Approach 2:
The training data is segmented into multiple diverse sources including structured electronic health record data, unstructured clinical notes, medical imaging data, laboratory results, and treatment outcome data. This segmentation allows the AI system to process different types of clinical information through specialized processing pipelines, extracting meaningful patterns from each data source while maintaining the ability to integrate insights across multiple modalities, thereby achieving comprehensive context awareness from real-world clinical environments.
Data Source
AI summary
An artificial intelligence enabled advanced treatment management system (AI-ATMS) is configured to improve inter-actions and outcomes from a multidisciplinary patient care team. The AI-ATMS includes advanced integration with an artificial intelligence system with access to federated databases. The AI-ATMS is configured for the real-time engagement with the patient-treatment team in a manner that provides the patient-treatment team with context-related data elements, while also permitting the patient-treatment team with the ability to train the AI-ATMS during the engagement. The AI-ATMS may be for treating a cancer or other medical condition.