AI Dispatch System for Healthcare
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Solution Overview
Problem
Healthcare providers face challenges in selecting the optimal AI algorithm for medical tasks due to difficulties in comparing the performance, inclusion and exclusion criteria, and costs of various AI vendors, as each AI is validated on proprietary and non-public small cohorts, making it hard to choose the best solution for specific patient needs.
Innovation Solution
A method and system for AI dispatch using multi-objective optimization that selects the most suitable AI from a group based on patient-specific, user-specific, and task-specific information, including medical images, user-defined constraints, AI performance, and pricing information, to provide a Pareto-optimal solution that balances multiple objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple AI algorithms are applied to obtain the best results, then diagnostic accuracy is improved, but application costs increase
Solution Approach 1:
The system performs preliminary selection of the optimal AI algorithm before application by comparing performance characteristics, inclusion/exclusion criteria, and costs against patient-specific data. This preliminary action identifies the single best AI to apply, avoiding the need to run multiple AIs and thereby reducing costs while maintaining diagnostic accuracy.
Solution Approach 2:
The system automatically selects and applies the optimal AI algorithm without requiring manual intervention to test multiple AIs. The multi-objective optimization framework autonomously evaluates available AIs against patient data and constraints, self-determining the best choice and eliminating wasteful application of suboptimal algorithms.
2Measurement precision
If AI vendors use proprietary validation cohorts, then vendor-specific performance data is obtained, but comparability between different AI algorithms deteriorates
Solution Approach 1:
The system introduces an intermediary multi-objective optimization framework that mediates between vendor-specific performance data and the need for comparability. This framework standardizes the evaluation by considering multiple objectives (performance, costs, inclusion/exclusion criteria) and patient-specific data, enabling fair comparison of otherwise proprietary algorithms.
Solution Approach 2:
The optimization framework serves multiple functions simultaneously: it evaluates vendor-specific performance data, applies inclusion/exclusion criteria, considers cost constraints, and selects the optimal AI for each patient. This multi-functional approach enables comparability across different vendors while preserving the value of proprietary validation data.
3Ease of operation
If a single AI vendor is chosen for all patients, then operational simplicity is improved, but optimal matching to patient-specific needs deteriorates
Solution Approach 1:
The system dynamically selects the optimal AI algorithm for each patient based on patient-specific data, clinical constraints, and AI performance characteristics. Rather than a static single-vendor approach, the system adapts the AI selection to match each patient's unique needs while maintaining operational simplicity through automated optimization.
Data Source
AI summary
Patient, user, and/or AI information are used in a multi-objective optimization to select one of a plurality of available AIs for a task. On a patient or user-specific basis, an optimal AI is selected and applied for medical imaging or other healthcare actions. The selection may be before application, avoiding costs of applying multiple AIs to obtain the best results. The optimization may be based on statistical feedback from the user for various of the available AIs, providing information not otherwise available. The optimization may be based on AI performance, AI inclusion and/or exclusion criteria, and/or pricing information. By using optimization based on various information related to the patient, user, and/or available AI, the application of AI for a given user and/or patient by the computer may be improved. The computer operates better to provide more focused information through AI application.