AI Surgical Guidance System with Local Processing
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Solution Overview
Problem
Telementoring in surgery is hindered by the unavailability of expert mentors and reliability issues with computer networks, leading to disruptions in medical care and limited telemonitoring expertise.
Innovation Solution
A system utilizing artificially intelligent medical mentoring frameworks that include machine learning models to assess and intervene in multi-step medical procedures, providing real-time guidance through image and sensor data analysis, and ensuring failover during communication disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time telemonitoring with expert mentors is implemented, then medical guidance quality is improved, but system reliability deteriorates due to network dependencies
Solution Approach 1:
An AI assistant is introduced as an intermediary between the care provider and the telemonitoring system. The AI assistant locally processes medical procedure data and provides guidance without requiring constant connection to remote experts, thereby maintaining guidance quality while reducing dependency on unreliable network connections.
Solution Approach 2:
The system pre-loads medical procedure data and AI models into local memory before procedures begin. This preliminary action enables the AI assistant to provide real-time guidance during procedures without requiring network connectivity, resolving the contradiction between quality guidance and network reliability.
2Measurement precision
If expert mentors are made available for telemonitoring, then care quality is improved, but availability deteriorates due to expert unavailability
Solution Approach 1:
The AI assistant enables the care provider to perform procedures with autonomous guidance based on pre-loaded medical data and machine learning models. This self-service capability eliminates the need for constant expert mentor availability while maintaining high care quality through AI-driven real-time feedback and intervention.
Solution Approach 2:
The system creates a local copy of expert knowledge in the form of pre-loaded medical procedure data and trained AI models. This copying allows the AI assistant to replicate expert-level guidance capabilities locally, making care quality independent of actual expert availability.
3Reliability
If AI assistant with local processing is implemented, then system reliability is improved during network failures, but device complexity increases
Solution Approach 1:
The system architecture is segmented into modular components: local AI processing unit, data storage module, and communication interface. This segmentation allows the core AI functionality to operate independently with high reliability during network failures, while the increased complexity is confined to specific modules rather than the entire system.
Data Source
AI summary
A system and methods for artificially intelligent medical procedure assessment and intervention are provided. A system may acquire image data from a camera targeting a location where healthcare is administered. The system may receive sensor data from a sensor attached to a care provider during performance of a procedure. The system may generate gesture features based on the sensor data. The system may generate image features based on the image data. The system may determine, based on the image features and a machine learning model, a step identifier for a step in a multi-step surgical procedure. The system may access text descriptive of an instruction to perform the step. The system may display the text on a display accessible to the care provider. The system may determine, based on the gesture information and a second machine learning model, a performance metric that measures performance of the care provider.