Video-Based AI Clinical Assessment for Asymptomatic Risk Screening
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Solution Overview
Problem
Clinical and sub-clinical process decisions rely heavily on symptomatic diagnosis, neglecting asymptomatic risk factors, leading to inefficient healthcare delivery and increased costs due to undiagnosed conditions, and fragmented healthcare systems hinder effective care coordination.
Innovation Solution
An AI-driven, automated method for medical assessment using a computer system with a central database, processor, user interface, and camera, allowing patients to perform assessments through a patient portal, analyze video frames for key points, and diagnose health conditions, including asymptomatic risks, providing a risk value for musculoskeletal injuries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional symptomatic diagnosis methods are used, then clinical decisions can be made with current information, but asymptomatic risk factors are neglected leading to inefficient healthcare delivery and increased costs
Solution Approach 1:
The system performs preliminary analysis of patient data using AI/ML algorithms to identify asymptomatic risk factors before clinical decisions are made. This preliminary action enables early detection of hidden risks without delaying healthcare delivery, resolving the contradiction between detection precision and productivity.
Solution Approach 2:
An AI-driven intermediary system acts as a mediator between raw patient data and clinical decision-making. This intermediary automatically analyzes data to uncover asymptomatic risk factors, improving detection precision while maintaining healthcare delivery efficiency by automating the analysis process.
2Measurement precision
If comprehensive medical data analysis is performed to identify asymptomatic risk factors, then early detection is enabled, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex data analysis task into multiple specialized AI/ML models, each trained to detect specific types of risk factors. This segmentation improves identification accuracy for different risk categories while managing system complexity through modular architecture.
Solution Approach 2:
A universal AI-driven platform is implemented that can analyze multiple types of medical data (clinical, sub-clinical, wearable device data) and identify various risk factors through a single integrated system, reducing overall system complexity while maintaining comprehensive detection capabilities.
3Productivity
If AI-driven automated assessment is implemented, then diagnosis speed and standardization improve, but implementation costs and technical infrastructure requirements increase
Solution Approach 1:
The system enables self-service automated assessments where patients can complete evaluations at home using wearable devices and mobile applications. This self-service approach accelerates diagnosis speed while reducing the need for complex clinical infrastructure, as the AI system processes data automatically without requiring extensive technical support.
Solution Approach 2:
Traditional manual diagnostic processes are replaced with AI-driven automated analysis systems. This substitution increases diagnosis speed and standardization while the modular software architecture manages technical infrastructure complexity through cloud-based processing and standardized data interfaces.
4Adaptability or versatility
If fragmented healthcare data from multiple sources is integrated, then comprehensive care coordination is achieved, but data integration complexity and privacy security requirements increase
Solution Approach 1:
A universal data integration platform is implemented that can connect with multiple healthcare data sources (electronic health records, wearable devices, clinical systems) through standardized interfaces. This universal approach achieves comprehensive care coordination while managing integration complexity through a single unified system architecture.
Solution Approach 2:
An AI-driven data intermediary layer is introduced between fragmented healthcare data sources and the analysis system. This intermediary automatically standardizes and secures data from multiple sources, enabling comprehensive care coordination while simplifying integration complexity and managing privacy security requirements through automated protocols.
Data Source
AI summary
A computer system for automatically performing medical diagnoses having a main portal configured to allow data communication with a user device, an AI bot in data communication with the main portal, the AI bot being configured to guide the user through a medical assessment, perform the medical assessment and diagnose the patient, wherein the AI bot is further configured to receive video data of the patient performing a physical activity, analyze each individual frame of the video using a custom trained model in order to diagnose the patient, a patient portal in data communication with a processing and communication module and the AI bot, a central database in data communication with the processing and communication module, an internal database, the AI bot and the patient portal, the central database being configured to facilitate an interconnection of the AI bot with the internal database and an external database.