AI Clinical Assessment Portal for Asymptomatic Risk Detection
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Solution Overview
Problem
Clinical and sub-clinical process decisions in healthcare rely heavily on symptom-based diagnosis, often ignoring asymptomatic risk factors, leading to inefficient and costly healthcare delivery systems, and patients lack access to comprehensive health data due to fragmented systems and privacy concerns.
Innovation Solution
An AI-driven platform for medical data collection and analysis that allows patients to perform self-assessments through a patient portal, using a camera to capture physical activities, analyze key points, and provide automated diagnoses, including risk assessments for musculoskeletal injuries, leveraging machine learning algorithms to identify asymptomatic risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If symptom-based diagnosis is used, then healthcare delivery is simplified, but asymptomatic risk factors are missed leading to higher costs
Solution Approach 1:
The system segments the diagnostic process into multiple independent components: symptom assessment, risk factor analysis, and predictive modeling. Each component processes specific data types separately before integrating results, allowing comprehensive evaluation without overwhelming complexity
Solution Approach 2:
The system performs preliminary risk assessment by analyzing asymptomatic risk factors before clinical symptoms manifest. Predictive models evaluate potential health issues in advance, enabling preventive interventions before conditions deteriorate
2Loss of information
If comprehensive medical data collection is implemented, then health insights are improved, but system complexity and privacy concerns increase
Solution Approach 1:
The platform serves multiple functions through a unified architecture: data collection from diverse sources, secure storage, AI-driven analysis, predictive modeling, and patient-provider communication. This multi-functionality reduces the need for separate specialized systems
Solution Approach 2:
The system introduces an intermediary AI layer that processes and anonymizes medical data before storage and analysis. This intermediary protects patient privacy while enabling comprehensive data utilization for predictive analytics
3Productivity
If AI-driven automated diagnosis is used, then diagnostic speed and affordability are improved, but reliance on traditional medical judgment is reduced
Solution Approach 1:
The system implements feedback loops where AI diagnostic results are continuously refined based on outcomes and provider adjustments. Clinical feedback from providers and actual patient outcomes feed back into the predictive models, improving their accuracy and alignment with clinical judgment over time
Solution Approach 2:
The system transforms subjective clinical parameters into objective quantifiable metrics through AI analysis. By converting traditional qualitative assessments into measurable data points, the system maintains clinical judgment integrity while enabling automated processing and faster diagnostics
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.