AI-driven dynamic surgical model download and adaptive minimally invasive surgery control methods

By integrating an in-vehicle AI system with cloud collaboration, the system enables dynamic downloading and adaptive control of AI surgical models, solving the problems of existing technologies being unable to adapt to different diseases and network limitations, and improving the automation and safety of the emergency medical system.

CN122337486APending Publication Date: 2026-07-03龙麟灵
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
龙麟灵
Filing Date
2026-03-30
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing AI medical surgery systems cannot dynamically update and adapt to different patient conditions, cannot be used in weak or offline network scenarios, lack model security verification, cannot achieve full-process automation, cannot cope with complex emergency scenarios involving multiple diseases, and lack a network outage redundancy operation mechanism.

Method used

The vehicle-mounted AI system collects vital signs and trauma data in real time, uses edge computing for automatic symptom identification and risk classification, combines local and cloud model libraries for matching and downloading, establishes an encrypted communication link, realizes adaptive control and offline autonomy of the model, constructs a full-process encrypted verification system, and supports rapid switching of multiple diseases and offline operation.

Benefits of technology

It enables dynamic downloading and adaptive adjustment of surgical models, ensuring the safety and continuity of surgery, improving the success rate of emergency care, reducing the probability of surgical errors, and adapting to the emergency care needs of remote areas.

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Abstract

This invention discloses an AI-powered dynamic surgical model download and adaptive minimally invasive surgical control method, relating to the fields of medical AI and emergency rescue technology. It collects patient data through multimodal sensors, automatically identifies symptoms and matches surgical models, and downloads encrypted models from the cloud via satellite link when no effective model is available locally. After multiple verifications, the model is loaded and run. During surgery, surgical parameters are adaptively adjusted based on real-time feedback, and a network outage autonomous redundancy mechanism is also included. This invention solves the problems of existing surgical models being fixed, unusable under weak network conditions, and lacking adaptive control, enabling autonomous emergency rescue for all diseases and in all scenarios, without the need for professional personnel. It is compatible with vehicle-mounted emergency rescue systems, significantly improving the survival rate of patients with sudden critical illnesses.
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