AI-Enabled Individualized Medical Modeling for Precision Diagnosis
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Solution Overview
Problem
Existing medical digital twins are not personalized to individual patients, relying on generic representations and lacking advanced AI and machine learning capabilities for precise diagnostics, prognostics, and therapeutics, which are essential for personalized medicine.
Innovation Solution
A medical modeling architecture utilizing individualized medical modeling (IMM) with 13 levels and 80 categories, incorporating AI, ML, and GenAI, including 3D geometric deep learning, personal health assistants, and an integrated health record platform, to analyze patient-specific data for diagnostics, prognostics, and therapeutics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If generic digital twins are used for medical modeling, then computational resources and time are reduced, but personalization and diagnostic precision deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing patient data (genomic, proteomic, metabolomic, clinical records) before modeling is needed. Personalized medical models are pre-configured with patient-specific data structures and relationships, so that when diagnostics are needed, the model is already prepared and only requires final execution, significantly reducing actual diagnostic time while maintaining full personalization.
Solution Approach 2:
The invention creates simplified copies or representations of complex biological systems at multiple scales (molecular, cellular, tissue, organ levels). These computational models copy essential biological relationships and interactions, allowing rapid simulation and analysis without requiring full complexity of the actual biological systems, thus reducing computational time while preserving diagnostic accuracy.
2Measurement precision
If advanced AI and machine learning are integrated into medical modeling, then diagnostic and therapeutic capabilities are improved, but system complexity increases
Solution Approach 1:
The system segments the complex medical modeling task into distinct modular components: data acquisition module, data preprocessing module, model configuration module, simulation engine module, and analysis module. Each module handles a specific function and can be independently developed, tested, and optimized. This segmentation reduces overall system complexity by breaking down the monolithic complex system into manageable pieces while maintaining advanced AI capabilities in each segment.
Solution Approach 2:
The invention introduces intermediary layers between raw patient data and final diagnostic conclusions. These include data normalization layers, feature extraction layers, and model interpretation layers that mediate between complex AI processing and clinically actionable insights. The intermediaries simplify the interface between advanced machine learning algorithms and clinical users, reducing perceived system complexity while preserving diagnostic accuracy.
3Manufacturing precision
If personalized patient data is analyzed at multiple biological levels, then treatment precision is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant and informative data features from multi-level patient data (genomic variants, protein expressions, metabolite concentrations, clinical parameters). Rather than processing all raw data, the model identifies and extracts key biomarkers and critical features that drive diagnostic and therapeutic decisions. This extraction reduces data processing requirements by focusing computational resources on the most informative subset of data while maintaining treatment precision.
Solution Approach 2:
The invention applies local quality by treating different data types and biological levels with specialized processing methods appropriate to their nature. Genomic data receives different processing than proteomic data, which receives different processing than clinical records. Each data source is processed with locally optimized algorithms and validation rules, reducing overall processing requirements by avoiding uniform over-processing while maintaining high treatment precision through appropriately tailored analysis.
Data Source
AI summary
Systems and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular disease, cancer, neurological disorders, immune system disorders and genetic diseases.