Mobile Disease Management System Using Machine Learning Image Analysis
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Solution Overview
Problem
Traditional healthcare access methods are often delayed and not cost-effective, especially for minor issues, necessitating a solution for affordable and accessible medical care leveraging new technologies.
Innovation Solution
A mobile device-based system using machine learning for image analysis and cloud-based data storage, allowing users to upload images of their conditions for diagnosis and treatment by healthcare providers, with secure HIPAA-compliant data management and payment processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional healthcare access methods are used (contact provider, make appointment, visit), then healthcare providers can provide comprehensive care, but delays occur and costs increase for minor issues
Solution Approach 1:
The patent extracts the diagnostic function from the traditional in-person visit by implementing image upload and machine learning-based analysis. Users can upload images of their conditions through a mobile interface, and the system automatically analyzes them using machine learning algorithms, separating the diagnostic capability from the physical presence requirement and eliminating appointment delays.
Solution Approach 2:
The patent introduces an intermediary system consisting of a mobile interface and machine learning analysis platform that mediates between the user and healthcare provider. This intermediary enables asynchronous communication where users can submit their conditions via images and receive automated analysis without needing immediate provider availability, thus reducing delays while maintaining accessibility.
2Reliability
If traditional in-person visits are used for minor issues, then comprehensive examination is possible, but it is not cost-effective for frequent minor visits
Solution Approach 1:
The patent replaces the mechanical system of in-person physical examination with an automated image-based analysis system. Instead of requiring physical presence for examination, the system uses machine learning algorithms to analyze uploaded images, maintaining diagnostic reliability while simplifying the interaction process and reducing costs for frequent minor visits.
Solution Approach 2:
The patent implements self-service functionality where users can independently upload their condition images and receive automated analysis without requiring provider intervention for initial assessment. The machine learning system performs the diagnostic analysis autonomously, enabling users to manage minor conditions independently while maintaining access to provider expertise when needed.
Data Source
AI summary
There is a dire need to reduce healthcare costs and appointment times with specialists. The instant mobile device method, process and the system addresses this need. The system and mobile application allows the user/patient to interact with health care providers who are certified to work in a particular geographical region without hesitation. In the instant application a novel mobile technology powered by unique image analysis software based on machine learning process to evaluate the submitted images for diagnostic purposes. The ease of approaching a health care provider by using the mobile device and getting matched to the right healthcare provider is another feature of this mobile application. The ease of providing case history and images for diagnosis and treatment is also novel.