Surgical Robot Task Selection Using Multi-Modal Risk Assessment
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Solution Overview
Problem
Conventional medical systems lack the ability to efficiently process large sets of different data modalities to determine task assignments and perform risk assessments for medical procedures, failing to consider the quality of data in task selection and execution.
Innovation Solution
A computer-assisted system utilizing machine learning models to analyze data streams, generate tasks, and select automated tasks based on risk values, incorporating rules to control the task generation and selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems are used for monitoring medical environments, then system simplicity is maintained, but the ability to process large sets of different data modalities and perform risk assessment is insufficient
Solution Approach 1:
The system segments the complex task processing into multiple specialized machine learning models: a task generation model that creates potential tasks from multi-modal data, a risk assessment model that evaluates each task's safety, and a task selection model that chooses optimal tasks. This segmentation allows each component to specialize in processing specific data types and functions, improving overall adaptability while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary layer of machine learning models between the raw multi-modal data sources and the final task execution. These intermediary models process and interpret data from cameras, sensors, and other sources, transforming complex multi-modal inputs into structured task representations with associated risk values, thereby enabling sophisticated data processing without directly complicating the control system.
2Productivity
If manual task selection is used in medical procedures, then personnel oversight is maintained, but productivity and efficiency are reduced
Solution Approach 1:
The system implements feedback loops where the risk assessment model continuously evaluates generated tasks and provides risk values back to the task selection model. This feedback mechanism enables automated task selection by allowing the system to iteratively refine task choices based on safety assessments, significantly improving productivity while maintaining controlled automation that can override decisions when necessary.
Solution Approach 2:
The machine learning models operate autonomously to generate, assess, and select tasks without requiring constant manual intervention. The task generation model automatically creates potential tasks from sensor data, the risk assessment model independently evaluates each task's safety, and the selection model autonomously chooses the best tasks to execute, enabling the system to serve itself in task management while improving efficiency.
3Reliability
If comprehensive risk assessment is performed on all potential tasks, then safety is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial risk assessment by focusing computational resources on evaluating only the most promising tasks generated by the task generation model. Rather than performing exhaustive risk assessment on all possible tasks, the system selectively assesses a subset of high-priority tasks, maintaining safety through targeted evaluation while reducing processing time and computational overhead.
Solution Approach 2:
The task generation model performs preliminary filtering by generating only relevant and feasible tasks from the multi-modal data before risk assessment begins. This preliminary action reduces the number of tasks requiring comprehensive risk evaluation, thereby maintaining high safety standards through focused assessment while minimizing the time and computational resources required for task selection.
Data Source
AI summary
Systems and methods are described for selecting tasks for using artificial intelligence. The system may include one or more repositionable structures configured to support respective instruments, and a control system operably coupled to the repositionable structure, the control system configured to receive a plurality of data streams; analyze, using a task generation machine learning model, the data streams to identify one or more tasks that may be performed by the one or more repositionable structures and generate respective risk values for the tasks, wherein a task generation constitution including a plurality of rules is input into the task generation machine learning model to control how the task generation machine learning model analyzes the data steams to identify the tasks; select, using a task selection machine learning model, an automated task based on the respective risk values; and control the one or more repositionable structures to perform the selected automated task.