Autonomous Medical Robotics With Neural Feedback Learning
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Solution Overview
Problem
Current medical procedures require significant user intervention and are not efficiently automated, limiting the ability to robotically diagnose and treat medical conditions without continuous human guidance.
Innovation Solution
A learning/evolving system architecture that utilizes neural networks and sensor systems to develop and improve base logic/models/procedures for robotic systems to perform medical procedures autonomously, allowing continuous training and adaptation based on feedback and sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If medical procedures are automated using robotic systems, then productivity and efficiency are improved, but the system requires high reliability and precision to ensure patient safety
Solution Approach 1:
The patent implements multiple feedback mechanisms including sensor systems that continuously monitor procedural parameters, neural networks that learn from procedural outcomes, and systems that provide feedback to users about robotic system status and decision-making. This feedback enables continuous improvement and validation of automated procedures while maintaining safety
Solution Approach 2:
The robotic system performs self-diagnosis, self-calibration, and self-improvement through machine learning algorithms that automatically analyze procedural data and update their decision-making capabilities without requiring constant human intervention or reprogramming
2Extent of automation
If robotic systems perform medical procedures autonomously, then user intervention is reduced, but the system complexity increases
Solution Approach 1:
The patent divides the autonomous robotic system into distinct modular components including sensor systems, neural networks, base logic/models/procedures, and robotic actuators. Each module performs a specific function and can be independently developed, tested, and validated, reducing overall system complexity while maintaining autonomous capability
Solution Approach 2:
The patent introduces intermediary systems including base logic/models/procedures that translate complex medical decision-making into actionable robotic commands, and user interface systems that provide simplified oversight and control to medical professionals without requiring them to directly operate the complex robotic system
3Measurement precision
If neural networks are used to improve procedural outcomes, then measurement precision and diagnostic accuracy are improved, but the system requires significant training data and computational resources
Solution Approach 1:
The patent implements preliminary action by pre-training neural networks with extensive training data during system development, and by pre-establishing base logic/models/procedures that encode medical knowledge before actual procedures begin. This allows the system to operate with high accuracy during procedures without requiring real-time data collection or computation
Data Source
AI summary
Embodiments of architecture, systems, and methods to develop a learning/evolving system to robotically perform one or more activities of a medical procedure where the medical procedure may include diagnosing a patient's medical condition(s), treating medical condition(s), and robotically diagnosing a patient's medical condition(s) and performing one or more medical procedure activities based on the diagnosis without User intervention.