Oncological Foundation Model With Interpretable Multimodal Predictions
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Solution Overview
Problem
Existing medical AI models are limited in scope and capability, requiring large amounts of data for training and often producing non-interpretable results, making it difficult for clinicians to understand their predictions.
Innovation Solution
A deep learning-based oncological foundation model trained with multimodal data, including radiology, pathology, genetics, and patient history, using a transformer architecture to enhance flexibility and interpretability, and employing supervised and self-supervised learning to provide transparent predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used to make medical predictions, then prediction capability is improved, but interpretability deteriorates
Solution Approach 1:
The patent introduces an interpretability layer that acts as an intermediary between the deep learning model's internal representations and the clinician's understanding. This layer generates explanations that bridge the gap between complex model predictions and human-comprehensible medical reasoning, allowing clinicians to understand why predictions are made without simplifying the underlying model architecture.
2Measurement precision
If deep learning models with many layers and parameters are used, then prediction accuracy is improved, but transparency deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that provide clinicians with information about model confidence, prediction uncertainty, and the basis for predictions. This feedback loop allows clinicians to understand model behavior and make informed decisions about whether to trust or override model predictions, maintaining transparency despite model complexity.
3Loss of information
If simple classifier models are used, then interpretability is improved, but prediction capability deteriorates
Solution Approach 1:
The patent segments the prediction system into multiple specialized components, each handling specific aspects of the prediction task. This segmentation allows the use of complex models for specific sub-tasks where they excel, while maintaining overall system interpretability through modular design and clear delineation of each component's function and contribution to the final prediction.
4Quantity of substance
If models are trained on limited data types, then training data requirements are reduced, but versatility deteriorates
Solution Approach 1:
The patent implements a unified model architecture that can process multiple data types (radiology images, pathology images, genetic data, clinical data) through a single framework. This universal architecture is designed to accept diverse input modalities and produce integrated predictions across multiple cancer types, reducing the need for separate specialized models while maintaining versatility.
Data Source
AI summary
An oncological foundation model is trained with broad, multimodal data to make predictions concerning a variety of different types of cancers. For example, the foundation model may make use of medical images drawn from radiology and pathology, as well as immunohistochemistry data; the presence or absence of biomarkers for particular diagnoses; patient history data; patient demographic data; and other forms of medical data. When using medical images, whole medical images as well as feature sets derived from the medical images may be used. The foundation model may have both causal predictive abilities as well as generative abilities.