Dynamic Biomedical Graph Segregates IVF Inputs for Transparent AI Decisions
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Solution Overview
Problem
Current AI systems in medical diagnostics lack transparency and adaptability, often treating inputs as a 'black box' and failing to effectively segregate and analyze diverse data inputs, leading to inefficiencies in decision-making processes, especially in complex applications like IVF.
Innovation Solution
A dynamic biomedical graph combined with neural networks that allows for the segregation of inputs into manageable groups, enabling the discovery of new relationships and associations, and is adaptable through structure evolution logic, providing a clear decision path and confidence scoring for improved decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a typical black box neural network is used, then processing speed may be improved, but transparency and interpretability of the decision process deteriorates
Solution Approach 1:
The patent segments the neural network into a graph structure with discrete nodes representing decision steps. Each node processes specific inputs independently, allowing the decision path to be visualized and traced. This segmentation maintains processing efficiency while enabling transparency by showing which inputs led to which conclusions through the graph nodes.
2Reliability
If all inputs are processed together in a single neural network, then comprehensive analysis is achieved, but system complexity and difficulty of managing diverse data types increases
Solution Approach 1:
The patent divides the input data into multiple discrete groups, with each group fed to separate neural network nodes in the graph. This segmentation allows comprehensive analysis of all inputs while reducing system complexity by organizing diverse data types into manageable, independent processing units that can be developed and maintained separately.
Solution Approach 2:
The graph structure serves multiple functions simultaneously: it organizes diverse inputs, directs data flow, enables parallel processing, and provides interpretability. This multi-functionality achieves comprehensive analysis without proportionally increasing system complexity, as the same structural framework supports multiple operational requirements.
3Stability of the object's composition
If the biomedical graph structure is made rigid and fixed, then system stability is improved, but adaptability and ability to discover new relationships deteriorates
Solution Approach 1:
The patent implements a dynamic graph structure where nodes and connections can be added, removed, or modified based on new data and insights. This dynamic capability allows the system to maintain stability through its established decision paths while simultaneously adapting to discover new relationships by incorporating additional nodes and connections into the existing graph framework.
4Reliability
If confidence levels are assigned to every parameter and node, then decision reliability is improved, but computational complexity and processing time increases
Solution Approach 1:
The patent applies confidence level assessment selectively to critical nodes and parameters in the decision path rather than uniformly to every element. This partial application maintains decision reliability for key conclusions while reducing computational overhead by omitting detailed confidence calculations from less critical processing steps, thereby reducing processing time.
Data Source
AI summary
A decision system includes a dynamic biomedical graph configured to provide multiple functions. Through dynamic variation of the biomedical graph structure a more optimal segregation of a diverse set of inputs can be identified. A decision process by which a conclusion is reached can be viewed as a path through the biomedical graph. In contrast with a typical “black box” neural network, this approach clarifies how and why a particular conclusion was reached. Such clarification can lead to identification of new and/or unexpected relationships between input data and resulting conclusions, and also provide confidence that the conclusion was based on a sensible decision process. The decision system is applied, as an example, to medical decisions made in In Vitro Fertilization (IVF).